In [1]:
%run NADINE_classification_sea.ipynb
Number of input:  3
Number of output:  2
Number of batch:  100
All Data
100% (100 of 100) |######################| Elapsed Time: 0:03:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  92.04646464646466 (+/-) 7.245762441583441
Testing Loss:  0.24809447032484142 (+/-) 0.17327467956558293
Precision:  0.9209290579001399
Recall:  0.9204646464646464
F1 score:  0.9196410650965958
Testing Time:  0.002747598320546776 (+/-) 0.004207666574854691
Training Time:  1.9877013413593023 (+/-) 0.03938866113608207


=== Average network evolution ===
Total hidden node:  9.080808080808081 (+/-) 2.5412824555178
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=13, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 13
No. of parameters : 52

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 13
No. of output : 2
No. of parameters : 28
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:03:16 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.59494949494949 (+/-) 7.566611583531633
Testing Loss:  0.2566300773560399 (+/-) 0.17824953720371742
Precision:  0.9166665640997699
Recall:  0.915949494949495
F1 score:  0.914941185008031
Testing Time:  0.0026094070588699495 (+/-) 0.0008937288137483986
Training Time:  1.9765822381684275 (+/-) 0.0707110478646652


=== Average network evolution ===
Total hidden node:  10.818181818181818 (+/-) 2.0068752351270844
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=14, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 14
No. of parameters : 56

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=14, out_features=2, bias=True)
)
No. of inputs : 14
No. of output : 2
No. of parameters : 30
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:03:18 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.86060606060607 (+/-) 7.252903538149439
Testing Loss:  0.25204279887104275 (+/-) 0.17577178453303247
Precision:  0.9190163401241277
Recall:  0.9186060606060606
F1 score:  0.9177674044007206
Testing Time:  0.0025177387276081125 (+/-) 0.0007885767857022272
Training Time:  1.9954934240591646 (+/-) 0.05999641203153533


=== Average network evolution ===
Total hidden node:  9.292929292929292 (+/-) 2.1379331341063037
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 12
No. of parameters : 48

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 12
No. of output : 2
No. of parameters : 26
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:03:13 ETA:  00:00:00

=== Performance result ===
Accuracy:  92.0050505050505 (+/-) 7.388461844781541
Testing Loss:  0.24668141902245658 (+/-) 0.17388963722523088
Precision:  0.9206835395137225
Recall:  0.920050505050505
F1 score:  0.9191573991246542
Testing Time:  0.002729856606685754 (+/-) 0.004720559681870977
Training Time:  1.953931415923918 (+/-) 0.03738359701839315


=== Average network evolution ===
Total hidden node:  5.525252525252525 (+/-) 1.6957667694087522
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 8
No. of parameters : 32

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:03:16 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.7111111111111 (+/-) 7.244125295070801
Testing Loss:  0.2522579489106482 (+/-) 0.17495033080632222
Precision:  0.9177422975813482
Recall:  0.9171111111111111
F1 score:  0.9161582395155624
Testing Time:  0.0025168886088361643 (+/-) 0.0006976634724533672
Training Time:  1.9813676434333878 (+/-) 0.08373462430746167


=== Average network evolution ===
Total hidden node:  10.171717171717171 (+/-) 2.0550032762656874
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=13, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 13
No. of parameters : 52

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 13
No. of output : 2
No. of parameters : 28
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  92.13183673469389
Std Accuracy:  6.800135554912209
Hidden Node mean 9.00204081632653
Hidden Node std:  2.7888253540651995
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% Data
100% (100 of 100) |######################| Elapsed Time: 0:01:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  90.84848484848483 (+/-) 8.553342587637163
Testing Loss:  0.26605809062267793 (+/-) 0.17459025784965898
Precision:  0.9097661796436708
Recall:  0.9084848484848485
F1 score:  0.9071095578262883
Testing Time:  0.0029125526697948725 (+/-) 0.005091869841401095
Training Time:  0.934997760888302 (+/-) 0.09522105697186267


=== Average network evolution ===
Total hidden node:  9.0 (+/-) 1.964328347956588
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 12
No. of parameters : 48

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 12
No. of output : 2
No. of parameters : 26
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:01:34 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.20505050505052 (+/-) 7.760693160458192
Testing Loss:  0.2693159354831835 (+/-) 0.17317394021816723
Precision:  0.9125088757185745
Recall:  0.912050505050505
F1 score:  0.9110555739384203
Testing Time:  0.0029651468450372868 (+/-) 0.004600721437592303
Training Time:  0.9536460914997139 (+/-) 0.08014681759194285


=== Average network evolution ===
Total hidden node:  8.878787878787879 (+/-) 1.9083692719798533
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 12
No. of parameters : 48

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 12
No. of output : 2
No. of parameters : 26
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:01:40 ETA:  00:00:00

=== Performance result ===
Accuracy:  90.88686868686868 (+/-) 8.760282806013896
Testing Loss:  0.268101196472693 (+/-) 0.1766333648120577
Precision:  0.9097953334219658
Recall:  0.9088686868686868
F1 score:  0.907621646110993
Testing Time:  0.0022969101414535985 (+/-) 0.000856577416973236
Training Time:  1.0072671789111514 (+/-) 0.01612809927573245


=== Average network evolution ===
Total hidden node:  5.565656565656566 (+/-) 1.7242279265395608
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 8
No. of parameters : 32

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:01:39 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.02727272727276 (+/-) 8.235618848302094
Testing Loss:  0.2678614988019972 (+/-) 0.17114389009308614
Precision:  0.9110367155350154
Recall:  0.9102727272727272
F1 score:  0.909118276531031
Testing Time:  0.0028200558941773694 (+/-) 0.004830555760948444
Training Time:  0.9993915028042264 (+/-) 0.012765175556011258


=== Average network evolution ===
Total hidden node:  4.696969696969697 (+/-) 1.5403957608653134
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 7
No. of parameters : 28

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:01:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  90.03333333333333 (+/-) 9.84078300243482
Testing Loss:  0.28078381371016453 (+/-) 0.190405711632873
Precision:  0.9025464377352657
Recall:  0.9003333333333333
F1 score:  0.8984366335839261
Testing Time:  0.0023282802466190224 (+/-) 0.0007924938211500075
Training Time:  0.9931801160176595 (+/-) 0.01360737271221153


=== Average network evolution ===
Total hidden node:  5.747474747474747 (+/-) 2.3925111880022305
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=10, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 10
No. of parameters : 40

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 10
No. of output : 2
No. of parameters : 22
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  91.07775510204083
Std Accuracy:  8.26208793231163
Hidden Node mean 6.795918367346939
Hidden Node std:  2.6432786678262072
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% Data
100% (100 of 100) |######################| Elapsed Time: 0:00:51 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.24747474747475 (+/-) 9.529140245852187
Testing Loss:  0.3055352962227783 (+/-) 0.16683987778905285
Precision:  0.8953348558420224
Recall:  0.8924747474747474
F1 score:  0.8901141465922442
Testing Time:  0.0024453654433741713 (+/-) 0.000899871271626218
Training Time:  0.5140980614556206 (+/-) 0.012067787521766522


=== Average network evolution ===
Total hidden node:  7.434343434343434 (+/-) 1.7417140991749975
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=10, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 10
No. of parameters : 40

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 10
No. of output : 2
No. of parameters : 22
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:00:52 ETA:  00:00:00

=== Performance result ===
Accuracy:  88.13535353535357 (+/-) 10.856793368612582
Testing Loss:  0.3240241948703323 (+/-) 0.17700968466881314
Precision:  0.8866859549087837
Recall:  0.8813535353535353
F1 score:  0.8778278949417561
Testing Time:  0.0025330577233825067 (+/-) 0.0047486244661193635
Training Time:  0.5220378601189816 (+/-) 0.021475664792920037


=== Average network evolution ===
Total hidden node:  2.7777777777777777 (+/-) 1.0876306892846717
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 5
No. of parameters : 20

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:00:51 ETA:  00:00:00

=== Performance result ===
Accuracy:  88.9040404040404 (+/-) 10.413481153569217
Testing Loss:  0.3052358489596482 (+/-) 0.17532217613379503
Precision:  0.8922409791889865
Recall:  0.8890404040404041
F1 score:  0.8864468519580697
Testing Time:  0.0023789622566916728 (+/-) 0.0008365835694093035
Training Time:  0.5202701766081531 (+/-) 0.019487244057278474


=== Average network evolution ===
Total hidden node:  6.737373737373737 (+/-) 1.6671869405270094
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 9
No. of parameters : 36

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 9
No. of output : 2
No. of parameters : 20
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:00:51 ETA:  00:00:00

=== Performance result ===
Accuracy:  87.13939393939395 (+/-) 12.324893247898112
Testing Loss:  0.3274126123599332 (+/-) 0.19862582560956255
Precision:  0.8796176920553247
Recall:  0.8713939393939394
F1 score:  0.8665672992318848
Testing Time:  0.0020268517311173256 (+/-) 0.0008924830360491278
Training Time:  0.5135503898967396 (+/-) 0.01374306886910887


=== Average network evolution ===
Total hidden node:  3.1717171717171717 (+/-) 1.4567183545396163
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 6
No. of parameters : 24

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
100% (100 of 100) |######################| Elapsed Time: 0:00:51 ETA:  00:00:00

=== Performance result ===
Accuracy:  90.77777777777777 (+/-) 7.828871141138848
Testing Loss:  0.28165094990922945 (+/-) 0.16283372396354528
Precision:  0.9092196717509192
Recall:  0.9077777777777778
F1 score:  0.9063332375599721
Testing Time:  0.0031206294743701666 (+/-) 0.004686521857226626
Training Time:  0.5138730954642248 (+/-) 0.018132758133208943


=== Average network evolution ===
Total hidden node:  7.929292929292929 (+/-) 1.5907086614165276
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=11, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 11
No. of parameters : 44

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=11, out_features=2, bias=True)
)
No. of inputs : 11
No. of output : 2
No. of parameters : 24
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  89.08469387755102
Std Accuracy:  10.126261594778247
Hidden Node mean 5.6204081632653065
Hidden Node std:  2.676197393030636
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 89% (89 of 100) |####################   | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  67.47474747474747 (+/-) 7.314885106968543
Testing Loss:  0.5595584740542402 (+/-) 0.04018779356611432
Precision:  0.7680273804304095
Recall:  0.6747474747474748
F1 score:  0.583256426337157
Testing Time:  0.00295170389040552 (+/-) 0.0018365735723885267
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 5
No. of parameters : 20

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
 99% (99 of 100) |###################### | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  67.3010101010101 (+/-) 7.308380341917092
Testing Loss:  0.5659842734987085 (+/-) 0.04105067714654622
Precision:  0.7670244652694179
Recall:  0.6730101010101011
F1 score:  0.5798968120305256
Testing Time:  0.0020083176969277737 (+/-) 0.005079080709222568
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 3
No. of parameters : 12

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
 92% (92 of 100) |#####################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  63.347474747474756 (+/-) 7.370091668280475
Testing Loss:  0.6104204203143264 (+/-) 0.024466945101211213
Precision:  0.7489237630283784
Recall:  0.6334747474747475
F1 score:  0.4948721992567769
Testing Time:  0.00159186546248619 (+/-) 0.0007225927284669849
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 6
No. of parameters : 24

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
 82% (82 of 100) |##################     | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  65.50505050505052 (+/-) 7.413301909370037
Testing Loss:  0.6115767817304592 (+/-) 0.025476510849458286
Precision:  0.7591664775237807
Recall:  0.6550505050505051
F1 score:  0.5431859017795544
Testing Time:  0.0018591447310014205 (+/-) 0.0007259296935056934
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  8.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 8
No. of parameters : 32

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
 90% (90 of 100) |####################   | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  65.53636363636365 (+/-) 7.3618379148097395
Testing Loss:  0.5628098797316503 (+/-) 0.049897174156126864
Precision:  0.7586683659827788
Recall:  0.6553636363636364
F1 score:  0.5439290938022063
Testing Time:  0.002275642722544044 (+/-) 0.00475496788604065
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 7
No. of parameters : 28

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  65.82306122448979
Std Accuracy:  7.540065691285761
Hidden Node mean 5.8
Hidden Node std:  1.7204650534085253
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [2]:
%run NADINE_classification_hyperplane.ipynb
Number of input:  4
Number of output:  2
Number of batch:  120
All Data
100% (120 of 120) |######################| Elapsed Time: 0:03:32 ETA:  00:00:00

=== Performance result ===
Accuracy:  92.15042016806723 (+/-) 2.8990254505530983
Testing Loss:  0.29649631203222676 (+/-) 0.05067234374492242
Precision:  0.9215042597324717
Recall:  0.9215042016806723
F1 score:  0.9215042160650396
Testing Time:  0.002300685193358349 (+/-) 0.000705926809147076
Training Time:  1.7797974277945126 (+/-) 0.03483840790539562


=== Average network evolution ===
Total hidden node:  4.722689075630252 (+/-) 0.5639651003550427
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 25

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:03:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  92.38655462184875 (+/-) 4.0025816116527615
Testing Loss:  0.28320085351206675 (+/-) 0.06635055100422449
Precision:  0.923880336030871
Recall:  0.9238655462184874
F1 score:  0.9238644855502037
Testing Time:  0.0020635949463403526 (+/-) 0.0008180129898153774
Training Time:  1.773071429308723 (+/-) 0.03246699468754135


=== Average network evolution ===
Total hidden node:  2.689075630252101 (+/-) 0.4628719110561482
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 3
No. of parameters : 15

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:03:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.92521008403362 (+/-) 6.052448889574097
Testing Loss:  0.2916631070004792 (+/-) 0.07360288900962149
Precision:  0.9192630322025698
Recall:  0.9192521008403362
F1 score:  0.919251206655974
Testing Time:  0.0023710387093680246 (+/-) 0.004508675639463742
Training Time:  1.767293781793418 (+/-) 0.022291765765204013


=== Average network evolution ===
Total hidden node:  2.6218487394957983 (+/-) 0.5019557822880026
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 3
No. of parameters : 15

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:03:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.5747899159664 (+/-) 7.0038071098149866
Testing Loss:  0.2941112954075597 (+/-) 0.09029855397836757
Precision:  0.9158714242448224
Recall:  0.9157478991596638
F1 score:  0.9157428938392136
Testing Time:  0.001949346366048861 (+/-) 0.0006170568800573533
Training Time:  1.7663562518207967 (+/-) 0.02545448962813301


=== Average network evolution ===
Total hidden node:  2.6470588235294117 (+/-) 0.47788461203740956
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 3
No. of parameters : 15

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:03:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  92.61680672268908 (+/-) 2.537063720916055
Testing Loss:  0.27964958961771313 (+/-) 0.0572829309409839
Precision:  0.926193076707485
Recall:  0.9261680672268907
F1 score:  0.9261664898737734
Testing Time:  0.0020519064254119618 (+/-) 0.0006537969101582346
Training Time:  1.7728120339016955 (+/-) 0.02470586330893761


=== Average network evolution ===
Total hidden node:  2.563025210084034 (+/-) 0.4960119180965951
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 3
No. of parameters : 15

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  92.36474576271186
Std Accuracy:  3.989555582884229
Hidden Node mean 3.047457627118644
Hidden Node std:  0.9782994467577771
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% Data
100% (120 of 120) |######################| Elapsed Time: 0:01:47 ETA:  00:00:00

=== Performance result ===
Accuracy:  90.95042016806723 (+/-) 5.672799228243759
Testing Loss:  0.3199200607648417 (+/-) 0.08689299819893508
Precision:  0.9097760950749174
Recall:  0.9095042016806723
F1 score:  0.9094871294846018
Testing Time:  0.0022505591897403494 (+/-) 0.004076026827974053
Training Time:  0.8989008494785854 (+/-) 0.009925686023113937


=== Average network evolution ===
Total hidden node:  2.0588235294117645 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=2, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 2
No. of parameters : 10

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 2
No. of output : 2
No. of parameters : 6
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:01:47 ETA:  00:00:00

=== Performance result ===
Accuracy:  90.75294117647057 (+/-) 6.817597392529412
Testing Loss:  0.3187672264185272 (+/-) 0.08624936502083748
Precision:  0.9076082111257892
Recall:  0.9075294117647059
F1 score:  0.9075237969767198
Testing Time:  0.0019830775861980534 (+/-) 0.0006825015700865035
Training Time:  0.8996561655477315 (+/-) 0.01522871386239103


=== Average network evolution ===
Total hidden node:  2.6554621848739495 (+/-) 0.5566552995522419
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 3
No. of parameters : 15

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:01:47 ETA:  00:00:00

=== Performance result ===
Accuracy:  88.30504201680674 (+/-) 10.15210247496055
Testing Loss:  0.36226710476795165 (+/-) 0.12782271646259816
Precision:  0.883458371358108
Recall:  0.8830504201680672
F1 score:  0.8830158091366551
Testing Time:  0.00185481840822877 (+/-) 0.0006891938453977504
Training Time:  0.901871108207382 (+/-) 0.016199070713873557


=== Average network evolution ===
Total hidden node:  2.1260504201680672 (+/-) 0.3319061791282604
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=2, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 2
No. of parameters : 10

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 2
No. of output : 2
No. of parameters : 6
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:01:47 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.19915966386552 (+/-) 5.392049793400228
Testing Loss:  0.3135913302417563 (+/-) 0.07213994704383872
Precision:  0.912071960078489
Recall:  0.9119915966386555
F1 score:  0.9119862179790043
Testing Time:  0.002477130970033277 (+/-) 0.004041488405764773
Training Time:  0.8993637742114668 (+/-) 0.014733840329427744


=== Average network evolution ===
Total hidden node:  3.7394957983193278 (+/-) 0.43890974309917974
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 4
No. of parameters : 20

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:01:47 ETA:  00:00:00

=== Performance result ===
Accuracy:  90.49747899159665 (+/-) 6.615190847363184
Testing Loss:  0.31880455658215434 (+/-) 0.07534365667646682
Precision:  0.90501207833305
Recall:  0.9049747899159664
F1 score:  0.9049733920030214
Testing Time:  0.002207309258084337 (+/-) 0.0006200870986661813
Training Time:  0.8995499450619481 (+/-) 0.010604531889417547


=== Average network evolution ===
Total hidden node:  4.1344537815126055 (+/-) 0.38728465955640046
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 4
No. of parameters : 20

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  90.64813559322035
Std Accuracy:  6.408370181910073
Hidden Node mean 2.9338983050847456
Hidden Node std:  0.9301193962890245
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% Data
100% (120 of 120) |######################| Elapsed Time: 0:00:55 ETA:  00:00:00

=== Performance result ===
Accuracy:  88.26554621848742 (+/-) 9.106027526132754
Testing Loss:  0.36755985109245076 (+/-) 0.11863846983396353
Precision:  0.8827062529273908
Recall:  0.8826554621848739
F1 score:  0.8826503110354769
Testing Time:  0.0020083619766876476 (+/-) 0.0007108639164210289
Training Time:  0.46482718291402864 (+/-) 0.011835648885369858


=== Average network evolution ===
Total hidden node:  2.3361344537815127 (+/-) 0.8723320519234269
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=2, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 2
No. of parameters : 10

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 2
No. of output : 2
No. of parameters : 6
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:00:55 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.3470588235294 (+/-) 6.665398215132213
Testing Loss:  0.33770105092465375 (+/-) 0.08491642032580225
Precision:  0.8934785696167269
Recall:  0.8934705882352941
F1 score:  0.8934695968065305
Testing Time:  0.002765956045198841 (+/-) 0.0041384509030663345
Training Time:  0.46240100740384654 (+/-) 0.011405785031031476


=== Average network evolution ===
Total hidden node:  5.621848739495798 (+/-) 0.4849256486135633
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 6
No. of parameters : 30

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:00:55 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.27815126050417 (+/-) 6.691423830896029
Testing Loss:  0.33938866002219065 (+/-) 0.08444331092705229
Precision:  0.8927837368943965
Recall:  0.892781512605042
F1 score:  0.8927811100686223
Testing Time:  0.002402982791932691 (+/-) 0.000732108476588942
Training Time:  0.4610254984943807 (+/-) 0.007079709366170477


=== Average network evolution ===
Total hidden node:  5.630252100840337 (+/-) 0.48273635685193506
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 25

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:00:55 ETA:  00:00:00

=== Performance result ===
Accuracy:  90.61344537815125 (+/-) 4.542847816142414
Testing Loss:  0.3321104891159955 (+/-) 0.06812160804703489
Precision:  0.9061632536475084
Recall:  0.9061344537815126
F1 score:  0.9061320780611632
Testing Time:  0.0026450838361467633 (+/-) 0.0007219116338995319
Training Time:  0.4614076754626106 (+/-) 0.007348057996985408


=== Average network evolution ===
Total hidden node:  8.504201680672269 (+/-) 0.49998234556784926
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 9
No. of parameters : 45

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 9
No. of output : 2
No. of parameters : 20
Dynamic laerning rate for each hidden layer:  [0.02]
100% (120 of 120) |######################| Elapsed Time: 0:00:55 ETA:  00:00:00

=== Performance result ===
Accuracy:  86.9529411764706 (+/-) 9.02386959357891
Testing Loss:  0.3890424458419575 (+/-) 0.11649172311489467
Precision:  0.8697079012034705
Recall:  0.8695294117647059
F1 score:  0.8695109646439012
Testing Time:  0.0023615861139377626 (+/-) 0.0041331417854312625
Training Time:  0.46273432058446545 (+/-) 0.010790581342372464


=== Average network evolution ===
Total hidden node:  3.4873949579831933 (+/-) 0.960265598924995
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=2, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 2
No. of parameters : 10

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 2
No. of output : 2
No. of parameters : 6
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  89.16237288135594
Std Accuracy:  6.9281304101181265
Hidden Node mean 5.110169491525424
Hidden Node std:  2.231454258295662
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 97% (117 of 120) |##################### | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  65.37899159663866 (+/-) 3.6903661531706033
Testing Loss:  0.6363926474787608 (+/-) 0.017221841824245285
Precision:  0.7111893381651403
Recall:  0.6537899159663866
F1 score:  0.6282848206557048
Testing Time:  0.0018342402802795923 (+/-) 0.0008572280771557304
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 7
No. of parameters : 35

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
 87% (105 of 120) |###################   | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  71.37563025210083 (+/-) 1.5731981228932126
Testing Loss:  0.6305836579378914 (+/-) 0.003307096053899442
Precision:  0.7945577330524731
Recall:  0.7137563025210084
F1 score:  0.6925057489860829
Testing Time:  0.0017085796644707688 (+/-) 0.0007305651249097896
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 25

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
 86% (104 of 120) |###################   | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  50.035294117647055 (+/-) 4.360278848443511
Testing Loss:  0.6929996649758154 (+/-) 0.005599663744895442
Precision:  0.5004176250587449
Recall:  0.5003529411764706
F1 score:  0.4996094878463812
Testing Time:  0.002037116459437779 (+/-) 0.004819470633333363
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 4
No. of parameters : 20

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
 97% (117 of 120) |##################### | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  77.30504201680674 (+/-) 2.2894294448854584
Testing Loss:  0.6188914159766766 (+/-) 0.005273408751214823
Precision:  0.7774206088596947
Recall:  0.7730504201680672
F1 score:  0.7721846291260479
Testing Time:  0.0014826049323843308 (+/-) 0.000694594772147449
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 3
No. of parameters : 15

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
 99% (119 of 120) |##################### | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  49.903361344537814 (+/-) 1.6931861851743946
Testing Loss:  0.6965975070200047 (+/-) 0.00217992601564544
Precision:  0.28527484247922935
Recall:  0.49903361344537817
F1 score:  0.3336821284924525
Testing Time:  0.0016177261576933019 (+/-) 0.0006838670486240806
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 4
No. of parameters : 20

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  62.80593220338983
Std Accuracy:  11.519702299979345
Hidden Node mean 4.6
Hidden Node std:  1.3564659966250538
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [3]:
%run NADINE_classification_weather.ipynb
Number of input:  8
Number of output:  2
Number of batch:  18
All Data
100% (18 of 18) |########################| Elapsed Time: 0:00:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  71.32941176470588 (+/-) 3.556727627846294
Testing Loss:  0.5403722156496609 (+/-) 0.04329136760952233
Precision:  0.6913702849107582
Recall:  0.7132941176470589
F1 score:  0.6890845092093629
Testing Time:  0.0016904297996969784 (+/-) 0.0005617839532356606
Training Time:  1.77819842450759 (+/-) 0.030996939894026028


=== Average network evolution ===
Total hidden node:  7.0588235294117645 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 8
No. of parameters : 72

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  71.67058823529412 (+/-) 3.1112253507888172
Testing Loss:  0.5406189823851866 (+/-) 0.04603602344216499
Precision:  0.6946387747005545
Recall:  0.7167058823529412
F1 score:  0.6865026228408925
Testing Time:  0.00197604123283835 (+/-) 0.000591063307092828
Training Time:  1.7654989466947668 (+/-) 0.009852571421042795


=== Average network evolution ===
Total hidden node:  6.0588235294117645 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 63

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  71.09411764705884 (+/-) 2.462774759406743
Testing Loss:  0.5428671065498801 (+/-) 0.04030093529778865
Precision:  0.6884534709358736
Recall:  0.7109411764705882
F1 score:  0.6604607546103711
Testing Time:  0.0019231824313893037 (+/-) 0.00040929424898422423
Training Time:  1.7815885543823242 (+/-) 0.01884339151193584


=== Average network evolution ===
Total hidden node:  6.9411764705882355 (+/-) 0.4159451654038515
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 8
No. of parameters : 72

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  71.70588235294119 (+/-) 3.48027561251984
Testing Loss:  0.5454000900773441 (+/-) 0.040067734513748746
Precision:  0.6952006889722699
Recall:  0.7170588235294117
F1 score:  0.6816144853217456
Testing Time:  0.0018056280472699333 (+/-) 0.00037103232072078693
Training Time:  1.7704437059514664 (+/-) 0.029176315804728674


=== Average network evolution ===
Total hidden node:  4.352941176470588 (+/-) 0.47788461203740945
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 45

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  70.69411764705882 (+/-) 2.981999050821505
Testing Loss:  0.5511516472872566 (+/-) 0.038062386796295644
Precision:  0.6819316781751159
Recall:  0.7069411764705882
F1 score:  0.6780131122610347
Testing Time:  0.0019874993492575255 (+/-) 0.00034309252911043936
Training Time:  1.7652401503394632 (+/-) 0.011489047161253723


=== Average network evolution ===
Total hidden node:  7.0588235294117645 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 8
No. of parameters : 72

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  71.21124999999999
Std Accuracy:  3.243223464009226
Hidden Node mean 6.3125
Hidden Node std:  1.0907995920424614
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% Data
100% (18 of 18) |########################| Elapsed Time: 0:00:15 ETA:  00:00:00

=== Performance result ===
Accuracy:  69.6058823529412 (+/-) 3.5470734562179893
Testing Loss:  0.5742993828128365 (+/-) 0.04396989162949954
Precision:  0.6640885048900774
Recall:  0.6960588235294117
F1 score:  0.6565126766136917
Testing Time:  0.0016372484319350298 (+/-) 0.00047316968865892476
Training Time:  0.8943946221295525 (+/-) 0.006913907754488262


=== Average network evolution ===
Total hidden node:  5.0588235294117645 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 54

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:15 ETA:  00:00:00

=== Performance result ===
Accuracy:  70.37647058823529 (+/-) 2.482297183650354
Testing Loss:  0.5670202146558201 (+/-) 0.029485407771790394
Precision:  0.6777116478403404
Recall:  0.703764705882353
F1 score:  0.6429458355402672
Testing Time:  0.001925510518691119 (+/-) 0.0005383760153305175
Training Time:  0.8952875978806439 (+/-) 0.007619511279858918


=== Average network evolution ===
Total hidden node:  5.705882352941177 (+/-) 0.5703152773430975
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 45

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:15 ETA:  00:00:00

=== Performance result ===
Accuracy:  70.51764705882353 (+/-) 2.3746789985218557
Testing Loss:  0.55905381546301 (+/-) 0.029895203191934623
Precision:  0.679285954206223
Recall:  0.7051764705882353
F1 score:  0.6485433007137573
Testing Time:  0.001978032729204963 (+/-) 0.000341632885297832
Training Time:  0.9023997783660889 (+/-) 0.012391396872824055


=== Average network evolution ===
Total hidden node:  6.529411764705882 (+/-) 0.4991341984846218
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 63

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:15 ETA:  00:00:00

=== Performance result ===
Accuracy:  70.17058823529412 (+/-) 3.13908280082731
Testing Loss:  0.5744826109970317 (+/-) 0.03653259881557109
Precision:  0.6741783808719745
Recall:  0.7017058823529412
F1 score:  0.6379157760150654
Testing Time:  0.0017485618591308594 (+/-) 0.000640537133346919
Training Time:  0.8928547746994916 (+/-) 0.003885215141072362


=== Average network evolution ===
Total hidden node:  6.647058823529412 (+/-) 0.47788461203740945
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 54

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:15 ETA:  00:00:00

=== Performance result ===
Accuracy:  69.72941176470589 (+/-) 3.123302307020257
Testing Loss:  0.5642991960048676 (+/-) 0.0296759048046517
Precision:  0.6671931684334511
Recall:  0.6972941176470588
F1 score:  0.622702931501586
Testing Time:  0.001676012487972484 (+/-) 0.0005692564746389187
Training Time:  0.9064657968633315 (+/-) 0.026105526707701558


=== Average network evolution ===
Total hidden node:  6.0588235294117645 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 63

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  69.91625
Std Accuracy:  3.004556695670761
Hidden Node mean 5.9875
Hidden Node std:  0.7157819151110204
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% Data
100% (18 of 18) |########################| Elapsed Time: 0:00:07 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.6201829209047205 (+/-) 0.02630606436972817
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.0016882700078627642 (+/-) 0.0005648622236766069
Training Time:  0.4569523334503174 (+/-) 0.006990880303626607


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 4
No. of parameters : 36

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:07 ETA:  00:00:00

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.090304173933049
Testing Loss:  0.5974845886230469 (+/-) 0.02406507834589242
Precision:  0.6277162034856335
Recall:  0.686
F1 score:  0.5591066246793293
Testing Time:  0.0015757644877714269 (+/-) 0.0004899215159543164
Training Time:  0.4609805696150836 (+/-) 0.007324266184726144


=== Average network evolution ===
Total hidden node:  4.470588235294118 (+/-) 0.4991341984846218
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 4
No. of parameters : 36

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:07 ETA:  00:00:00

=== Performance result ===
Accuracy:  67.90588235294116 (+/-) 4.1766114309309055
Testing Loss:  0.603587971014135 (+/-) 0.025530270864039875
Precision:  0.580652643603637
Recall:  0.6790588235294117
F1 score:  0.568524649612859
Testing Time:  0.001984540153952206 (+/-) 0.0004764899260866398
Training Time:  0.4569560920490938 (+/-) 0.008606815351798011


=== Average network evolution ===
Total hidden node:  6.9411764705882355 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 63

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:07 ETA:  00:00:00

=== Performance result ===
Accuracy:  68.9235294117647 (+/-) 3.6436131730765973
Testing Loss:  0.5910018892849193 (+/-) 0.02375912043887719
Precision:  0.6628754001376249
Recall:  0.6892352941176471
F1 score:  0.5765025082765465
Testing Time:  0.0017439056845272288 (+/-) 0.00041486780139656654
Training Time:  0.46376682730282054 (+/-) 0.005816481142477356


=== Average network evolution ===
Total hidden node:  3.9411764705882355 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 3
No. of parameters : 27

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
100% (18 of 18) |########################| Elapsed Time: 0:00:07 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.6077858910841101 (+/-) 0.031235013529145882
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.0018096110400031595 (+/-) 0.0005030625868849391
Training Time:  0.4586522859685561 (+/-) 0.005943431722970871


=== Average network evolution ===
Total hidden node:  4.705882352941177 (+/-) 0.4556450995538137
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 45

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  68.26500000000001
Std Accuracy:  4.026105438261646
Hidden Node mean 4.8375
Hidden Node std:  1.1666592261667499
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
N/A% (0 of 18) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--

=== Performance result ===
Accuracy:  62.26470588235294 (+/-) 4.457504072971064
Testing Loss:  0.6775683690519894 (+/-) 0.008139513963002729
Precision:  0.46418534062185385
Recall:  0.6226470588235294
F1 score:  0.5288608394023046
Testing Time:  0.001172949286068187 (+/-) 0.000506151804554063
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 4
No. of parameters : 36

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
N/A% (0 of 18) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.5995138673221364 (+/-) 0.03570022883740612
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.0012898865868063534 (+/-) 0.00045586888240667475
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 63

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
N/A% (0 of 18) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.6246076787219328 (+/-) 0.020225929634677627
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.0011144385618322035 (+/-) 0.0004697886688329447
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 45

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
N/A% (0 of 18) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.6027752862257116 (+/-) 0.027897091147674035
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.0009980482213637408 (+/-) 0.0005903526480058131
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 4
No. of parameters : 36

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
N/A% (0 of 18) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.6072112146545859 (+/-) 0.031461544096153905
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.0012955104603486903 (+/-) 0.0004516335999640168
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 54

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  67.0525
Std Accuracy:  4.883005606181505
Hidden Node mean 5.2
Hidden Node std:  1.1661903789690604
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [4]:
%run NADINE_classification_rfid.ipynb
Number of input:  3
Number of output:  4
Number of batch:  280
All Data
100% (280 of 280) |######################| Elapsed Time: 0:08:20 ETA:  00:00:00

=== Performance result ===
Accuracy:  98.50752688172042 (+/-) 5.178662520051795
Testing Loss:  0.09580279842993798 (+/-) 0.17195608986540695
Precision:  0.9850633741371198
Recall:  0.9850752688172043
F1 score:  0.9850357947701097
Testing Time:  0.004380185971550617 (+/-) 0.0030120721587800063
Training Time:  1.7873665944649755 (+/-) 0.05223200013442971


=== Average network evolution ===
Total hidden node:  24.03942652329749 (+/-) 5.708751474597842
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=29, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 29
No. of parameters : 116

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=29, out_features=4, bias=True)
)
No. of inputs : 29
No. of output : 4
No. of parameters : 120
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:08:19 ETA:  00:00:00

=== Performance result ===
Accuracy:  98.11612903225807 (+/-) 7.479002247716169
Testing Loss:  0.10154843126760803 (+/-) 0.19792186457054634
Precision:  0.9812388987408331
Recall:  0.9811612903225806
F1 score:  0.9811457087827173
Testing Time:  0.004506550382115081 (+/-) 0.0027239130705414287
Training Time:  1.784056090966775 (+/-) 0.026218185920194703


=== Average network evolution ===
Total hidden node:  25.096774193548388 (+/-) 6.163945082887561
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=30, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 30
No. of parameters : 120

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=30, out_features=4, bias=True)
)
No. of inputs : 30
No. of output : 4
No. of parameters : 124
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:08:19 ETA:  00:00:00

=== Performance result ===
Accuracy:  98.28064516129032 (+/-) 6.4155488438074775
Testing Loss:  0.10940202236122128 (+/-) 0.19013106980344696
Precision:  0.9827822837362377
Recall:  0.9828064516129033
F1 score:  0.9827886087842048
Testing Time:  0.0042245294030849225 (+/-) 0.003184458347739251
Training Time:  1.7818412464579374 (+/-) 0.024887894636771296


=== Average network evolution ===
Total hidden node:  21.232974910394265 (+/-) 5.522147208097677
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=26, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 26
No. of parameters : 104

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=26, out_features=4, bias=True)
)
No. of inputs : 26
No. of output : 4
No. of parameters : 108
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:08:20 ETA:  00:00:00

=== Performance result ===
Accuracy:  98.45949820788529 (+/-) 5.683697471937239
Testing Loss:  0.08817090074061065 (+/-) 0.16505240263601564
Precision:  0.9845958961883633
Recall:  0.9845949820788531
F1 score:  0.9845619718805048
Testing Time:  0.004862935739606084 (+/-) 0.003218650852716822
Training Time:  1.7874196780625211 (+/-) 0.026007434419859567


=== Average network evolution ===
Total hidden node:  27.53405017921147 (+/-) 5.529142901974678
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=32, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 32
No. of parameters : 128

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=32, out_features=4, bias=True)
)
No. of inputs : 32
No. of output : 4
No. of parameters : 132
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:08:20 ETA:  00:00:00

=== Performance result ===
Accuracy:  98.28637992831541 (+/-) 6.305538398396073
Testing Loss:  0.08944008701337387 (+/-) 0.1681688310345236
Precision:  0.9829407479345981
Recall:  0.9828637992831541
F1 score:  0.9828278053312793
Testing Time:  0.004851790739216685 (+/-) 0.004626798879340142
Training Time:  1.784758467828074 (+/-) 0.023383059830753682


=== Average network evolution ===
Total hidden node:  25.3584229390681 (+/-) 5.500132844955836
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=30, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 30
No. of parameters : 120

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=30, out_features=4, bias=True)
)
No. of inputs : 30
No. of output : 4
No. of parameters : 124
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  98.54136690647482
Std Accuracy:  5.122668720717178
Hidden Node mean 24.71726618705036
Hidden Node std:  5.961258957099992
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% Data
100% (280 of 280) |######################| Elapsed Time: 0:04:12 ETA:  00:00:00

=== Performance result ===
Accuracy:  96.81362007168458 (+/-) 10.31655735758229
Testing Loss:  0.1706754369894877 (+/-) 0.2565843639948921
Precision:  0.9681081032009339
Recall:  0.9681362007168459
F1 score:  0.9680816026197927
Testing Time:  0.00449659662007431 (+/-) 0.00370217445706961
Training Time:  0.8991863932660831 (+/-) 0.014419004085141813


=== Average network evolution ===
Total hidden node:  22.351254480286737 (+/-) 6.060500547402051
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=28, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 28
No. of parameters : 112

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=28, out_features=4, bias=True)
)
No. of inputs : 28
No. of output : 4
No. of parameters : 116
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:04:13 ETA:  00:00:00

=== Performance result ===
Accuracy:  96.31039426523297 (+/-) 12.13877637817133
Testing Loss:  0.18882475311343816 (+/-) 0.27630026207253383
Precision:  0.9633266934790227
Recall:  0.9631039426523298
F1 score:  0.9630218263627522
Testing Time:  0.004124692691269741 (+/-) 0.002931483495817556
Training Time:  0.9016470977482403 (+/-) 0.014624521056018365


=== Average network evolution ===
Total hidden node:  18.508960573476703 (+/-) 6.22738995550654
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=25, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 25
No. of parameters : 100

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=25, out_features=4, bias=True)
)
No. of inputs : 25
No. of output : 4
No. of parameters : 104
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:04:14 ETA:  00:00:00

=== Performance result ===
Accuracy:  96.1301075268817 (+/-) 12.078478015513117
Testing Loss:  0.19973284983506767 (+/-) 0.2788525744437138
Precision:  0.9612547351386822
Recall:  0.9613010752688173
F1 score:  0.9612121607371694
Testing Time:  0.0039303841129426035 (+/-) 0.002860409930768772
Training Time:  0.9060161609376203 (+/-) 0.016646389474722852


=== Average network evolution ===
Total hidden node:  17.182795698924732 (+/-) 5.86395264059698
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=23, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 23
No. of parameters : 92

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=23, out_features=4, bias=True)
)
No. of inputs : 23
No. of output : 4
No. of parameters : 96
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:04:14 ETA:  00:00:00

=== Performance result ===
Accuracy:  96.87777777777777 (+/-) 10.189899810310484
Testing Loss:  0.17213533477570633 (+/-) 0.24931515121299216
Precision:  0.9688457636287028
Recall:  0.9687777777777777
F1 score:  0.9686883428727033
Testing Time:  0.004207309428936264 (+/-) 0.0030012260209693418
Training Time:  0.9043187360182458 (+/-) 0.015718687615844565


=== Average network evolution ===
Total hidden node:  19.204301075268816 (+/-) 5.830138860709934
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=25, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 25
No. of parameters : 100

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=25, out_features=4, bias=True)
)
No. of inputs : 25
No. of output : 4
No. of parameters : 104
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:04:14 ETA:  00:00:00

=== Performance result ===
Accuracy:  96.715770609319 (+/-) 11.155356269976496
Testing Loss:  0.18115491464760783 (+/-) 0.26129834564887783
Precision:  0.9671166435898345
Recall:  0.96715770609319
F1 score:  0.9671122038415024
Testing Time:  0.004085470698640338 (+/-) 0.0029432420746605
Training Time:  0.9043886541892978 (+/-) 0.020007712086432053


=== Average network evolution ===
Total hidden node:  18.39068100358423 (+/-) 5.986369155445258
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=24, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 24
No. of parameters : 96

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=24, out_features=4, bias=True)
)
No. of inputs : 24
No. of output : 4
No. of parameters : 100
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  96.82784172661871
Std Accuracy:  10.368372374754307
Hidden Node mean 19.176978417266188
Hidden Node std:  6.198309314002664
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% Data
100% (280 of 280) |######################| Elapsed Time: 0:02:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  93.6189964157706 (+/-) 15.228214866657115
Testing Loss:  0.33884505900858125 (+/-) 0.3348806671384748
Precision:  0.9361037513949562
Recall:  0.9361899641577061
F1 score:  0.9359978299506898
Testing Time:  0.00348150516496337 (+/-) 0.002829386667201641
Training Time:  0.46274802419874406 (+/-) 0.009027895520982626


=== Average network evolution ===
Total hidden node:  13.480286738351255 (+/-) 5.120638393031646
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=20, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 20
No. of parameters : 80

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=20, out_features=4, bias=True)
)
No. of inputs : 20
No. of output : 4
No. of parameters : 84
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:02:12 ETA:  00:00:00

=== Performance result ===
Accuracy:  95.88315412186381 (+/-) 11.154639779832582
Testing Loss:  0.28391984310735513 (+/-) 0.2799758101066172
Precision:  0.9589744902632784
Recall:  0.958831541218638
F1 score:  0.9587437846740416
Testing Time:  0.003650169646013595 (+/-) 0.002533503164186266
Training Time:  0.4680409192184394 (+/-) 0.020443591328717942


=== Average network evolution ===
Total hidden node:  15.189964157706093 (+/-) 4.876588758534216
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=21, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 21
No. of parameters : 84

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=21, out_features=4, bias=True)
)
No. of inputs : 21
No. of output : 4
No. of parameters : 88
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:02:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  94.06236559139785 (+/-) 15.234006813035256
Testing Loss:  0.3155328658105652 (+/-) 0.32502563458095457
Precision:  0.9405964768572362
Recall:  0.9406236559139785
F1 score:  0.9404771155887688
Testing Time:  0.003660654936212793 (+/-) 0.002909406057386138
Training Time:  0.4616563063795849 (+/-) 0.01226758522684207


=== Average network evolution ===
Total hidden node:  15.017921146953405 (+/-) 5.137852498355462
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=21, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 21
No. of parameters : 84

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=21, out_features=4, bias=True)
)
No. of inputs : 21
No. of output : 4
No. of parameters : 88
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:02:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  93.63189964157705 (+/-) 16.38740731823018
Testing Loss:  0.32807887369586575 (+/-) 0.3424237038541921
Precision:  0.9362436201370711
Recall:  0.9363189964157707
F1 score:  0.9362084884709391
Testing Time:  0.003651742012270035 (+/-) 0.0026595894596306508
Training Time:  0.4596922884705246 (+/-) 0.010867386660802052


=== Average network evolution ===
Total hidden node:  13.982078853046595 (+/-) 5.093713320518899
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=20, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 20
No. of parameters : 80

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=20, out_features=4, bias=True)
)
No. of inputs : 20
No. of output : 4
No. of parameters : 84
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:02:09 ETA:  00:00:00

=== Performance result ===
Accuracy:  95.34229390681004 (+/-) 12.643833541993232
Testing Loss:  0.27852294273594375 (+/-) 0.2816820824808159
Precision:  0.9534864187566299
Recall:  0.9534229390681004
F1 score:  0.9533213179337134
Testing Time:  0.003716166301440167 (+/-) 0.002673435005717518
Training Time:  0.45873765364342695 (+/-) 0.009048368249529582


=== Average network evolution ===
Total hidden node:  14.824372759856631 (+/-) 4.773881398085375
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=21, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 21
No. of parameters : 84

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=21, out_features=4, bias=True)
)
No. of inputs : 21
No. of output : 4
No. of parameters : 88
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  94.7510071942446
Std Accuracy:  13.727235795406228
Hidden Node mean 14.530935251798562
Hidden Node std:  5.026274117017972
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
100% (280 of 280) |######################| Elapsed Time: 0:00:01 ETA:  00:00:00

=== Performance result ===
Accuracy:  42.89605734767025 (+/-) 1.4060362230435346
Testing Loss:  1.3622969594053043 (+/-) 0.003979283039759391
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.2261800316595075
Recall:  0.4289605734767025
F1 score:  0.29115950660400347
Testing Time:  0.0024084703042088445 (+/-) 0.0030716220587299367
Training Time:  3.574569592766437e-06 (+/-) 5.959998557920508e-05


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 5
No. of parameters : 20

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=4, bias=True)
)
No. of inputs : 5
No. of output : 4
No. of parameters : 24
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:00:01 ETA:  00:00:00

=== Performance result ===
Accuracy:  50.02329749103943 (+/-) 0.15425350716917904
Testing Loss:  1.2934244260138508 (+/-) 0.0028348320600007737
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.26727256695781665
Recall:  0.5002329749103943
F1 score:  0.343321343876092
Testing Time:  0.002903966493504022 (+/-) 0.002869679805746046
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  10.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=10, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 10
No. of parameters : 40

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=10, out_features=4, bias=True)
)
No. of inputs : 10
No. of output : 4
No. of parameters : 44
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:00:01 ETA:  00:00:00

=== Performance result ===
Accuracy:  33.95197132616487 (+/-) 1.2207237053359818
Testing Loss:  1.3639209261931826 (+/-) 0.0029279462833536906
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.25027469373097194
Recall:  0.33951971326164876
F1 score:  0.24837005205321017
Testing Time:  0.002465090016737634 (+/-) 0.0032990957450668426
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 6
No. of parameters : 24

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=4, bias=True)
)
No. of inputs : 6
No. of output : 4
No. of parameters : 28
Dynamic laerning rate for each hidden layer:  [0.02]
 98% (277 of 280) |##################### | Elapsed Time: 0:00:01 ETA:   0:00:00

=== Performance result ===
Accuracy:  52.696057347670255 (+/-) 2.143027088175485
Testing Loss:  1.2984814891678458 (+/-) 0.005127954142873944
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.43022215990094465
Recall:  0.5269605734767026
F1 score:  0.4518516442531176
Testing Time:  0.00260938965718806 (+/-) 0.0025831530530461794
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 7
No. of parameters : 28

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=4, bias=True)
)
No. of inputs : 7
No. of output : 4
No. of parameters : 32
Dynamic laerning rate for each hidden layer:  [0.02]
100% (280 of 280) |######################| Elapsed Time: 0:00:01 ETA:  00:00:00

=== Performance result ===
Accuracy:  41.3584229390681 (+/-) 2.985231093456183
Testing Loss:  1.3085512362927947 (+/-) 0.008579623438723972
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.4111094070040762
Recall:  0.413584229390681
F1 score:  0.3645665553775302
Testing Time:  0.0028447011038393957 (+/-) 0.0036859425281646698
Training Time:  3.5762786865234375e-06 (+/-) 5.9628481866835596e-05


=== Average network evolution ===
Total hidden node:  8.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 8
No. of parameters : 32

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=4, bias=True)
)
No. of inputs : 8
No. of output : 4
No. of parameters : 36
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  44.18553956834533
Std Accuracy:  6.8966574817875514
Hidden Node mean 7.2
Hidden Node std:  1.7204650534085255
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [5]:
%run NADINE_classification_rmnist.ipynb
Number of input:  784
Number of output:  10
Number of batch:  69
All Data
100% (69 of 69) |########################| Elapsed Time: 0:06:51 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.7779411764706 (+/-) 4.312732366740142
Testing Loss:  0.3656957885798286 (+/-) 0.1914550936991656
Precision:  0.8974027713792336
Recall:  0.8977794117647059
F1 score:  0.8974843421181354
Testing Time:  0.0048507276703329645 (+/-) 0.005889459167104931
Training Time:  6.037652418893926 (+/-) 0.07305288371021151


=== Average network evolution ===
Total hidden node:  19.720588235294116 (+/-) 3.8571766123273536
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=26, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 26
No. of parameters : 20410

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=26, out_features=10, bias=True)
)
No. of inputs : 26
No. of output : 10
No. of parameters : 270
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:06:50 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.8323529411765 (+/-) 3.920813285099023
Testing Loss:  0.3650701510555604 (+/-) 0.17736439110381158
Precision:  0.8978836143030622
Recall:  0.8983235294117647
F1 score:  0.8979715996898862
Testing Time:  0.0039026491782244515 (+/-) 0.0009169915944134823
Training Time:  6.029626842807321 (+/-) 0.08768680202650103


=== Average network evolution ===
Total hidden node:  21.073529411764707 (+/-) 3.915715607615444
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=27, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 27
No. of parameters : 21195

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=27, out_features=10, bias=True)
)
No. of inputs : 27
No. of output : 10
No. of parameters : 280
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:06:49 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.39999999999999 (+/-) 4.337422655692853
Testing Loss:  0.37944120311123486 (+/-) 0.19134794980701408
Precision:  0.8935722687340589
Recall:  0.894
F1 score:  0.8935782298699014
Testing Time:  0.004811756751116584 (+/-) 0.006440372476329324
Training Time:  6.020563612965977 (+/-) 0.08327891675695558


=== Average network evolution ===
Total hidden node:  20.397058823529413 (+/-) 3.8848866509407243
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=26, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 26
No. of parameters : 20410

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=26, out_features=10, bias=True)
)
No. of inputs : 26
No. of output : 10
No. of parameters : 270
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:06:46 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.04558823529413 (+/-) 4.743243920490844
Testing Loss:  0.3986700133365743 (+/-) 0.19186454445809153
Precision:  0.8902575075005407
Recall:  0.8904558823529412
F1 score:  0.8902066074912375
Testing Time:  0.0037770341424381033 (+/-) 0.0007749966947811903
Training Time:  5.977357026408701 (+/-) 0.06471926264580212


=== Average network evolution ===
Total hidden node:  18.191176470588236 (+/-) 3.5614659611440267
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=24, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 24
No. of parameters : 18840

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=24, out_features=10, bias=True)
)
No. of inputs : 24
No. of output : 10
No. of parameters : 250
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:06:48 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.65735294117647 (+/-) 4.327453951108197
Testing Loss:  0.36609419884488864 (+/-) 0.18164167439906312
Precision:  0.8961433158234535
Recall:  0.8965735294117647
F1 score:  0.8962467389087099
Testing Time:  0.004860043525695801 (+/-) 0.006537807966261404
Training Time:  6.0055275980164025 (+/-) 0.08049486239923274


=== Average network evolution ===
Total hidden node:  22.352941176470587 (+/-) 4.47464985775211
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=29, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 29
No. of parameters : 22765

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=29, out_features=10, bias=True)
)
No. of inputs : 29
No. of output : 10
No. of parameters : 300
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  89.8707462686567
Std Accuracy:  3.41583376917049
Hidden Node mean 20.459701492537313
Hidden Node std:  4.110854205509239
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% Data
100% (69 of 69) |########################| Elapsed Time: 0:03:25 ETA:  00:00:00

=== Performance result ===
Accuracy:  86.54411764705883 (+/-) 6.280323669837716
Testing Loss:  0.48717381476479416 (+/-) 0.25995288275951006
Precision:  0.864775883942096
Recall:  0.8654411764705883
F1 score:  0.8645916353388433
Testing Time:  0.003453356378218707 (+/-) 0.0007120042827385654
Training Time:  3.014961277737337 (+/-) 0.16742799389761504


=== Average network evolution ===
Total hidden node:  14.617647058823529 (+/-) 1.1249759705238398
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=16, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 16
No. of parameters : 12560

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=16, out_features=10, bias=True)
)
No. of inputs : 16
No. of output : 10
No. of parameters : 170
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:03:21 ETA:  00:00:00

=== Performance result ===
Accuracy:  86.55882352941177 (+/-) 7.188987396872215
Testing Loss:  0.4918177685536006 (+/-) 0.27929160682148796
Precision:  0.8651093602705155
Recall:  0.8655882352941177
F1 score:  0.8650604939743877
Testing Time:  0.004575571593116312 (+/-) 0.0062197466996679615
Training Time:  2.9522136239444507 (+/-) 0.15353610140787435


=== Average network evolution ===
Total hidden node:  17.16176470588235 (+/-) 2.7525939323484288
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=21, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 21
No. of parameters : 16485

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=21, out_features=10, bias=True)
)
No. of inputs : 21
No. of output : 10
No. of parameters : 220
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:03:20 ETA:  00:00:00

=== Performance result ===
Accuracy:  87.24411764705883 (+/-) 7.165406731876285
Testing Loss:  0.46989954898462577 (+/-) 0.27077468613807293
Precision:  0.8720543288004834
Recall:  0.8724411764705883
F1 score:  0.8716809854999056
Testing Time:  0.0036396033623639274 (+/-) 0.0009791138390918888
Training Time:  2.946575206868789 (+/-) 0.15999828949691033


=== Average network evolution ===
Total hidden node:  16.86764705882353 (+/-) 1.7481453345550657
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=19, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 19
No. of parameters : 14915

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=19, out_features=10, bias=True)
)
No. of inputs : 19
No. of output : 10
No. of parameters : 200
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:03:19 ETA:  00:00:00

=== Performance result ===
Accuracy:  88.18088235294118 (+/-) 5.507541817227729
Testing Loss:  0.4403886842157911 (+/-) 0.2376126597821049
Precision:  0.8813356969097015
Recall:  0.8818088235294118
F1 score:  0.8812343710882201
Testing Time:  0.004768171731163473 (+/-) 0.007270091557831491
Training Time:  2.923224883921006 (+/-) 0.1555575878756655


=== Average network evolution ===
Total hidden node:  19.455882352941178 (+/-) 2.1586612376888477
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=23, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 23
No. of parameters : 18055

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=23, out_features=10, bias=True)
)
No. of inputs : 23
No. of output : 10
No. of parameters : 240
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:03:21 ETA:  00:00:00

=== Performance result ===
Accuracy:  87.42352941176469 (+/-) 6.716860209403227
Testing Loss:  0.4575348578612594 (+/-) 0.2581767162026987
Precision:  0.873966356457187
Recall:  0.8742352941176471
F1 score:  0.8737147743115773
Testing Time:  0.0038860264946432676 (+/-) 0.0007411101108326854
Training Time:  2.9637258648872375 (+/-) 0.1902575565004077


=== Average network evolution ===
Total hidden node:  17.926470588235293 (+/-) 1.4981967246011236
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=20, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 20
No. of parameters : 15700

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=20, out_features=10, bias=True)
)
No. of inputs : 20
No. of output : 10
No. of parameters : 210
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  87.68985074626865
Std Accuracy:  5.214493433572249
Hidden Node mean 17.256716417910447
Hidden Node std:  2.4749023125963237
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% Data
100% (69 of 69) |########################| Elapsed Time: 0:01:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.35 (+/-) 9.54306801080363
Testing Loss:  0.6274096062954735 (+/-) 0.33494384504457314
Precision:  0.8333896816792239
Recall:  0.8335
F1 score:  0.8322249567950832
Testing Time:  0.004421023761524874 (+/-) 0.007179156693084881
Training Time:  1.4470381631570703 (+/-) 0.18496766943128867


=== Average network evolution ===
Total hidden node:  14.647058823529411 (+/-) 1.0539101686569952
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=16, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 16
No. of parameters : 12560

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=16, out_features=10, bias=True)
)
No. of inputs : 16
No. of output : 10
No. of parameters : 170
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:01:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.15294117647058 (+/-) 11.028885477639738
Testing Loss:  0.6457117298508391 (+/-) 0.3887588682191189
Precision:  0.831816971391776
Recall:  0.8315294117647059
F1 score:  0.8300960707539232
Testing Time:  0.003357806626488181 (+/-) 0.000703967535306976
Training Time:  1.44128116088755 (+/-) 0.18491195258754953


=== Average network evolution ===
Total hidden node:  14.382352941176471 (+/-) 1.9928505431158734
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=17, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 17
No. of parameters : 13345

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=17, out_features=10, bias=True)
)
No. of inputs : 17
No. of output : 10
No. of parameters : 180
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:01:39 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.82205882352943 (+/-) 8.922754217172475
Testing Loss:  0.6299682180671131 (+/-) 0.3446054502776884
Precision:  0.8379168691773826
Recall:  0.8382205882352941
F1 score:  0.8365749464367875
Testing Time:  0.004208729547612807 (+/-) 0.005610241499072107
Training Time:  1.4526439028627731 (+/-) 0.18853573138181765


=== Average network evolution ===
Total hidden node:  14.588235294117647 (+/-) 0.9886903714100947
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=16, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 16
No. of parameters : 12560

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=16, out_features=10, bias=True)
)
No. of inputs : 16
No. of output : 10
No. of parameters : 170
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:01:39 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.36911764705881 (+/-) 9.046826830001862
Testing Loss:  0.6119038605076426 (+/-) 0.34689859804912615
Precision:  0.8426964650930981
Recall:  0.8436911764705882
F1 score:  0.8424288780709651
Testing Time:  0.003506635918336756 (+/-) 0.0006323586922781164
Training Time:  1.4529242901241077 (+/-) 0.18544866815226194


=== Average network evolution ===
Total hidden node:  15.102941176470589 (+/-) 1.3841508958282494
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=17, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 17
No. of parameters : 13345

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=17, out_features=10, bias=True)
)
No. of inputs : 17
No. of output : 10
No. of parameters : 180
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:01:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.67500000000001 (+/-) 8.374530276555287
Testing Loss:  0.5915400480084559 (+/-) 0.33630332331912927
Precision:  0.8461173734452627
Recall:  0.84675
F1 score:  0.8457453334313139
Testing Time:  0.004223998855142032 (+/-) 0.0060989665834394816
Training Time:  1.4461341465220732 (+/-) 0.18632423017219257


=== Average network evolution ===
Total hidden node:  16.397058823529413 (+/-) 1.086544791886228
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=17, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 17
No. of parameters : 13345

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=17, out_features=10, bias=True)
)
No. of inputs : 17
No. of output : 10
No. of parameters : 180
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  84.50358208955224
Std Accuracy:  7.9630894217498085
Hidden Node mean 15.056716417910447
Hidden Node std:  1.5179742865103385
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 94% (65 of 69) |######################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  60.69705882352942 (+/-) 4.467923935191537
Testing Loss:  1.5691660221885233 (+/-) 0.06338841915685982
Precision:  0.6598437727719552
Recall:  0.6069705882352942
F1 score:  0.6021829635981736
Testing Time:  0.0024278900202582866 (+/-) 0.0007805335729344191
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  12.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 12
No. of parameters : 9420

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=12, out_features=10, bias=True)
)
No. of inputs : 12
No. of output : 10
No. of parameters : 130
Dynamic laerning rate for each hidden layer:  [0.02]
 95% (66 of 69) |######################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  65.37647058823529 (+/-) 4.270181069554547
Testing Loss:  1.3851431993877186 (+/-) 0.07066970467002194
Precision:  0.7040914077443862
Recall:  0.6537647058823529
F1 score:  0.6409908343494867
Testing Time:  0.0037265700452467974 (+/-) 0.006288660202837343
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  15.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=15, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 15
No. of parameters : 11775

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=15, out_features=10, bias=True)
)
No. of inputs : 15
No. of output : 10
No. of parameters : 160
Dynamic laerning rate for each hidden layer:  [0.02]
 85% (59 of 69) |####################    | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  65.28235294117648 (+/-) 5.276137769030057
Testing Loss:  1.4826031730455511 (+/-) 0.06977493707502204
Precision:  0.7142942373036114
Recall:  0.6528235294117647
F1 score:  0.6479459439205114
Testing Time:  0.0028160950716804058 (+/-) 0.0007043645295938386
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  14.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=14, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 14
No. of parameters : 10990

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=14, out_features=10, bias=True)
)
No. of inputs : 14
No. of output : 10
No. of parameters : 150
Dynamic laerning rate for each hidden layer:  [0.02]
 95% (66 of 69) |######################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  64.83823529411765 (+/-) 4.152994292278223
Testing Loss:  1.4382804264040554 (+/-) 0.06824306673319774
Precision:  0.7150713991808211
Recall:  0.6483823529411765
F1 score:  0.632373266129527
Testing Time:  0.0037043830927680522 (+/-) 0.006854867380029874
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  14.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=14, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 14
No. of parameters : 10990

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=14, out_features=10, bias=True)
)
No. of inputs : 14
No. of output : 10
No. of parameters : 150
Dynamic laerning rate for each hidden layer:  [0.02]
 89% (62 of 69) |#####################   | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  69.32794117647059 (+/-) 4.0978418131928365
Testing Loss:  1.4293343915658838 (+/-) 0.06539882975575784
Precision:  0.7169478817581189
Recall:  0.6932794117647059
F1 score:  0.6840222651883603
Testing Time:  0.0027276452849893007 (+/-) 0.0006963476442049866
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  14.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=14, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 14
No. of parameters : 10990

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=14, out_features=10, bias=True)
)
No. of inputs : 14
No. of output : 10
No. of parameters : 150
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  65.03492537313433
Std Accuracy:  5.240622434586708
Hidden Node mean 13.8
Hidden Node std:  0.9797958971132712
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [6]:
%run NADINE_classification_pmnist.ipynb
Number of input:  784
Number of output:  10
Number of batch:  69
All Data
100% (69 of 69) |########################| Elapsed Time: 0:06:51 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.43382352941177 (+/-) 13.971195265449865
Testing Loss:  0.546282297328991 (+/-) 0.4328766528412209
Precision:  0.8348656485393544
Recall:  0.8343382352941177
F1 score:  0.8342266216610447
Testing Time:  0.004340568009544821 (+/-) 0.005753344443770464
Training Time:  6.046676106312695 (+/-) 0.12881687404126776


=== Average network evolution ===
Total hidden node:  18.058823529411764 (+/-) 2.5718061457293144
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=22, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 22
No. of parameters : 17270

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=22, out_features=10, bias=True)
)
No. of inputs : 22
No. of output : 10
No. of parameters : 230
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:06:49 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.1970588235294 (+/-) 14.590024498913385
Testing Loss:  0.5515862648539683 (+/-) 0.44305466265403626
Precision:  0.8319739619568614
Recall:  0.8319705882352941
F1 score:  0.8315980159837275
Testing Time:  0.003988812951480641 (+/-) 0.0008364052576026348
Training Time:  6.018907413763158 (+/-) 0.07880498855890589


=== Average network evolution ===
Total hidden node:  19.602941176470587 (+/-) 3.010470482491326
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=24, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 24
No. of parameters : 18840

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=24, out_features=10, bias=True)
)
No. of inputs : 24
No. of output : 10
No. of parameters : 250
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:06:47 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.03529411764707 (+/-) 14.379496889524603
Testing Loss:  0.5702499383512665 (+/-) 0.44963685785500546
Precision:  0.8314390468429103
Recall:  0.8303529411764706
F1 score:  0.8304347357291668
Testing Time:  0.004570813740001005 (+/-) 0.006556913438598294
Training Time:  5.9818368448930626 (+/-) 0.10087611624547048


=== Average network evolution ===
Total hidden node:  15.617647058823529 (+/-) 1.645219561287882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=17, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 17
No. of parameters : 13345

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=17, out_features=10, bias=True)
)
No. of inputs : 17
No. of output : 10
No. of parameters : 180
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:06:46 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.12205882352941 (+/-) 14.284646689903232
Testing Loss:  0.5597732691642117 (+/-) 0.42725717543399905
Precision:  0.832537545123628
Recall:  0.8312205882352941
F1 score:  0.8314141384599958
Testing Time:  0.0037112165899837717 (+/-) 0.0007631935862286341
Training Time:  5.9789870065801285 (+/-) 0.05887288300015947


=== Average network evolution ===
Total hidden node:  17.647058823529413 (+/-) 3.3727485121116807
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=22, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 22
No. of parameters : 17270

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=22, out_features=10, bias=True)
)
No. of inputs : 22
No. of output : 10
No. of parameters : 230
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:07:02 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.13970588235296 (+/-) 12.908036025327084
Testing Loss:  0.7634805611827794 (+/-) 0.3704979447579078
Precision:  0.7697418307483593
Recall:  0.7713970588235294
F1 score:  0.7697300437761613
Testing Time:  0.005279881112715777 (+/-) 0.007035512056852779
Training Time:  6.211735024171717 (+/-) 1.157587575913904


=== Average network evolution ===
Total hidden node:  23.794117647058822 (+/-) 7.899290406570431
Number of layer:  1.4558823529411764 (+/-) 0.4980498300551794


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=18, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 18
No. of parameters : 14130

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=15, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 15
No. of parameters : 285

 3 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=15, out_features=10, bias=True)
)
No. of inputs : 15
No. of output : 10
No. of parameters : 160
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001]
Mean Accuracy:  82.87134328358208
Std Accuracy:  12.353004939342092
Hidden Node mean 19.035820895522388
Hidden Node std:  5.073069119998247
Hidden Layer mean:  1.0925373134328358
Hidden Layer std:  0.28978295163012774
50% Data
100% (69 of 69) |########################| Elapsed Time: 0:03:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.2014705882353 (+/-) 16.28436179326168
Testing Loss:  0.6970983947462895 (+/-) 0.4724680205227353
Precision:  0.7925648554943281
Recall:  0.7920147058823529
F1 score:  0.7915171975795074
Testing Time:  0.004289150238037109 (+/-) 0.007062555281055703
Training Time:  3.090509425191318 (+/-) 0.2580193687585982


=== Average network evolution ===
Total hidden node:  13.426470588235293 (+/-) 0.9125944990647178
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=14, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 14
No. of parameters : 10990

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=14, out_features=10, bias=True)
)
No. of inputs : 14
No. of output : 10
No. of parameters : 150
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:03:22 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.81911764705883 (+/-) 15.527577529265008
Testing Loss:  0.6885084801298731 (+/-) 0.48979780889463453
Precision:  0.7976199672398673
Recall:  0.7981911764705882
F1 score:  0.7973360027023904
Testing Time:  0.0034692427691291362 (+/-) 0.0007128913802272943
Training Time:  2.9678158584763024 (+/-) 0.1629995484955209


=== Average network evolution ===
Total hidden node:  14.573529411764707 (+/-) 1.1156134750017748
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=16, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 16
No. of parameters : 12560

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=16, out_features=10, bias=True)
)
No. of inputs : 16
No. of output : 10
No. of parameters : 170
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:03:20 ETA:  00:00:00

=== Performance result ===
Accuracy:  80.30588235294117 (+/-) 16.09669204556292
Testing Loss:  0.6618845344466322 (+/-) 0.46388060327505837
Precision:  0.8027461312808202
Recall:  0.8030588235294117
F1 score:  0.8021167475459899
Testing Time:  0.004239040262558881 (+/-) 0.006098644535893181
Training Time:  2.9429731298895443 (+/-) 0.15160792644656734


=== Average network evolution ===
Total hidden node:  15.264705882352942 (+/-) 2.30483919981942
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=19, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 19
No. of parameters : 14915

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=19, out_features=10, bias=True)
)
No. of inputs : 19
No. of output : 10
No. of parameters : 200
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:03:20 ETA:  00:00:00

=== Performance result ===
Accuracy:  80.52352941176471 (+/-) 15.801276173769104
Testing Loss:  0.6534879685324781 (+/-) 0.470075548367028
Precision:  0.8052034206139526
Recall:  0.805235294117647
F1 score:  0.8041090727520479
Testing Time:  0.0036011583664838005 (+/-) 0.0008115042590571057
Training Time:  2.9495151183184456 (+/-) 0.15283978600116885


=== Average network evolution ===
Total hidden node:  16.779411764705884 (+/-) 1.7050581173059773
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=18, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 18
No. of parameters : 14130

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=18, out_features=10, bias=True)
)
No. of inputs : 18
No. of output : 10
No. of parameters : 190
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:03:23 ETA:  00:00:00

=== Performance result ===
Accuracy:  80.13823529411764 (+/-) 16.693350467386395
Testing Loss:  0.6653229368521887 (+/-) 0.464202628984187
Precision:  0.800497870797109
Recall:  0.8013823529411764
F1 score:  0.8001602451166178
Testing Time:  0.004378034788019517 (+/-) 0.006396727333958313
Training Time:  2.9913469973732445 (+/-) 0.18330792891265285


=== Average network evolution ===
Total hidden node:  16.13235294117647 (+/-) 2.035742470167651
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=19, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 19
No. of parameters : 14915

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=19, out_features=10, bias=True)
)
No. of inputs : 19
No. of output : 10
No. of parameters : 200
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  80.91164179104479
Std Accuracy:  14.348879471256337
Hidden Node mean 15.274626865671642
Hidden Node std:  2.052108001258821
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% Data
100% (69 of 69) |########################| Elapsed Time: 0:01:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  74.87794117647059 (+/-) 18.44706931600467
Testing Loss:  0.8928117296274971 (+/-) 0.5125161033022954
Precision:  0.7490269541578192
Recall:  0.7487794117647059
F1 score:  0.7467287349254725
Testing Time:  0.003241721321554745 (+/-) 0.0006923762286360324
Training Time:  1.4421351306578691 (+/-) 0.18401023711539968


=== Average network evolution ===
Total hidden node:  12.117647058823529 (+/-) 0.6308120761625652
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=13, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 13
No. of parameters : 10205

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=13, out_features=10, bias=True)
)
No. of inputs : 13
No. of output : 10
No. of parameters : 140
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:01:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  76.87352941176471 (+/-) 17.054261306773835
Testing Loss:  0.8257875142290312 (+/-) 0.5118861190593397
Precision:  0.7691152618871726
Recall:  0.7687352941176471
F1 score:  0.7672772472609044
Testing Time:  0.004467497853671803 (+/-) 0.006342806043401021
Training Time:  1.4415201860315658 (+/-) 0.18527560243828003


=== Average network evolution ===
Total hidden node:  16.25 (+/-) 1.6122235285103468
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=18, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 18
No. of parameters : 14130

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=18, out_features=10, bias=True)
)
No. of inputs : 18
No. of output : 10
No. of parameters : 190
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:01:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  76.07941176470588 (+/-) 17.87125163350617
Testing Loss:  0.8214778891381096 (+/-) 0.5014818384573742
Precision:  0.762743532432833
Recall:  0.7607941176470588
F1 score:  0.75942443611301
Testing Time:  0.0036011303172392003 (+/-) 0.0006526407862191999
Training Time:  1.4488479915787191 (+/-) 0.18591746361674685


=== Average network evolution ===
Total hidden node:  15.25 (+/-) 0.7928912098063865
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=17, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 17
No. of parameters : 13345

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=17, out_features=10, bias=True)
)
No. of inputs : 17
No. of output : 10
No. of parameters : 180
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:01:39 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.99705882352943 (+/-) 17.30502282225865
Testing Loss:  0.8297660931506577 (+/-) 0.4981708867358865
Precision:  0.759132784843177
Recall:  0.7599705882352941
F1 score:  0.7574713918203102
Testing Time:  0.004442327162798713 (+/-) 0.0071704439128577515
Training Time:  1.4539881453794592 (+/-) 0.18805963960128272


=== Average network evolution ===
Total hidden node:  15.455882352941176 (+/-) 1.2418418902085793
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=17, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 17
No. of parameters : 13345

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=17, out_features=10, bias=True)
)
No. of inputs : 17
No. of output : 10
No. of parameters : 180
Dynamic laerning rate for each hidden layer:  [0.02]
100% (69 of 69) |########################| Elapsed Time: 0:01:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.72058823529412 (+/-) 17.78427559608512
Testing Loss:  0.8550452355514554 (+/-) 0.5045557174402614
Precision:  0.7580121618624277
Recall:  0.7572058823529412
F1 score:  0.7548307913460777
Testing Time:  0.003461006809683407 (+/-) 0.0006512643452812632
Training Time:  1.440460040288813 (+/-) 0.1854585688186084


=== Average network evolution ===
Total hidden node:  14.5 (+/-) 1.8510728208893603
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=17, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 17
No. of parameters : 13345

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=17, out_features=10, bias=True)
)
No. of inputs : 17
No. of output : 10
No. of parameters : 180
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  76.79671641791045
Std Accuracy:  16.272928383462904
Hidden Node mean 14.73134328358209
Hidden Node std:  1.933074957833799
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 85% (59 of 69) |####################    | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  23.248529411764707 (+/-) 26.190189522970908
Testing Loss:  2.0830139149637783 (+/-) 0.4251529294151486
Precision:  0.4797908331125918
Recall:  0.23248529411764707
F1 score:  0.247033387642271
Testing Time:  0.003578028258155374 (+/-) 0.007281525541163757
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  14.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=14, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 14
No. of parameters : 10990

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=14, out_features=10, bias=True)
)
No. of inputs : 14
No. of output : 10
No. of parameters : 150
Dynamic laerning rate for each hidden layer:  [0.02]
 86% (60 of 69) |####################    | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  23.46764705882353 (+/-) 25.61873419048044
Testing Loss:  2.090025575721965 (+/-) 0.38214799214450546
Precision:  0.43552230009184817
Recall:  0.2346764705882353
F1 score:  0.24772504730411182
Testing Time:  0.002779673127567067 (+/-) 0.0007856418923005174
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  13.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=13, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 13
No. of parameters : 10205

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=13, out_features=10, bias=True)
)
No. of inputs : 13
No. of output : 10
No. of parameters : 140
Dynamic laerning rate for each hidden layer:  [0.02]
 86% (60 of 69) |####################    | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  24.14117647058823 (+/-) 20.631281469809508
Testing Loss:  2.087069686721353 (+/-) 0.355490693508642
Precision:  0.4661501442132742
Recall:  0.24141176470588235
F1 score:  0.2430880855219304
Testing Time:  0.003593287047217874 (+/-) 0.006063696645095653
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  12.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 12
No. of parameters : 9420

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=12, out_features=10, bias=True)
)
No. of inputs : 12
No. of output : 10
No. of parameters : 130
Dynamic laerning rate for each hidden layer:  [0.02]
 92% (64 of 69) |######################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  24.51617647058824 (+/-) 21.474592444904292
Testing Loss:  2.1386852352058185 (+/-) 0.346134032465388
Precision:  0.49766362132335695
Recall:  0.24516176470588236
F1 score:  0.23431927755941334
Testing Time:  0.0025979210348690256 (+/-) 0.0007097978862626402
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  12.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 12
No. of parameters : 9420

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=12, out_features=10, bias=True)
)
No. of inputs : 12
No. of output : 10
No. of parameters : 130
Dynamic laerning rate for each hidden layer:  [0.02]
 86% (60 of 69) |####################    | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  19.075 (+/-) 22.934749995431414
Testing Loss:  2.1456482042284573 (+/-) 0.3726709008797878
Precision:  0.3988332615779539
Recall:  0.19075
F1 score:  0.18674455599811174
Testing Time:  0.003675239927628461 (+/-) 0.006296355685882603
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  13.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=784, out_features=13, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 784
No. of nodes : 13
No. of parameters : 10205

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=13, out_features=10, bias=True)
)
No. of inputs : 13
No. of output : 10
No. of parameters : 140
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  22.88059701492537
Std Accuracy:  23.729876871993902
Hidden Node mean 12.8
Hidden Node std:  0.7483314773547882
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [7]:
%run NADINE_classification_hepmass.ipynb
Number of input:  28
Number of output:  2
Number of batch:  2000
All Data
100% (2000 of 2000) |####################| Elapsed Time: 0:59:52 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.78509254627313 (+/-) 1.6814581557994008
Testing Loss:  0.33565192985319986 (+/-) 0.026867821059909737
Precision:  0.8391347537967414
Recall:  0.8378509254627313
F1 score:  0.8376962830723401
Testing Time:  0.009975884663217839 (+/-) 0.005409544062119003
Training Time:  1.7738356884865238 (+/-) 0.02726461092370268


=== Average network evolution ===
Total hidden node:  4.983991995997999 (+/-) 0.24652435795576202
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 5
No. of parameters : 145

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:59:56 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.93961980990494 (+/-) 1.6182275444821175
Testing Loss:  0.33559928182186394 (+/-) 0.027063566782921152
Precision:  0.8412817396643667
Recall:  0.8393961980990495
F1 score:  0.8391728227439491
Testing Time:  0.009880155608199607 (+/-) 0.005521819304928295
Training Time:  1.775860794786813 (+/-) 0.025612896446960798


=== Average network evolution ===
Total hidden node:  3.944472236118059 (+/-) 0.2746924408735865
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 4
No. of parameters : 116

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 1:00:14 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.11960980490245 (+/-) 1.6797697642993932
Testing Loss:  0.3324760759604341 (+/-) 0.026801875157202868
Precision:  0.8426674639399709
Recall:  0.8411960980490245
F1 score:  0.8410243671278081
Testing Time:  0.010260284394249431 (+/-) 0.005705364964115679
Training Time:  1.7843098704847113 (+/-) 0.033449489504207744


=== Average network evolution ===
Total hidden node:  5.901950975487744 (+/-) 0.37478183828856276
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 6
No. of parameters : 174

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 1:00:02 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.19574787393697 (+/-) 1.631541569317324
Testing Loss:  0.33170405247916335 (+/-) 0.02583321931339422
Precision:  0.8433637654415809
Recall:  0.8419574787393697
F1 score:  0.8417944466392715
Testing Time:  0.01040497918675219 (+/-) 0.005742835462958587
Training Time:  1.77863155155554 (+/-) 0.034280038986008565


=== Average network evolution ===
Total hidden node:  6.877938969484743 (+/-) 0.3580120676701238
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 7
No. of parameters : 203

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:59:37 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.30820410205101 (+/-) 1.524068278792782
Testing Loss:  0.3297780630527704 (+/-) 0.025375605231254043
Precision:  0.8448926775776725
Recall:  0.8430820410205102
F1 score:  0.8428746483999315
Testing Time:  0.00905679571085897 (+/-) 0.0054888112306791985
Training Time:  1.767761875832898 (+/-) 0.21528079700269565


=== Average network evolution ===
Total hidden node:  6.296648324162081 (+/-) 0.477134782515681
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 6
No. of parameters : 174

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  84.08202202202202
Std Accuracy:  1.5405656292328116
Hidden Node mean 5.601801801801802
Hidden Node std:  1.0914155692042598
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% Data
100% (2000 of 2000) |####################| Elapsed Time: 0:27:02 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.07928964482241 (+/-) 1.7856527023569115
Testing Loss:  0.3338425622515943 (+/-) 0.028417253480859106
Precision:  0.8422719072357879
Recall:  0.8407928964482241
F1 score:  0.8406196364678606
Testing Time:  0.007952979709459221 (+/-) 0.004475836587480246
Training Time:  0.7922585656966132 (+/-) 0.04383500680962723


=== Average network evolution ===
Total hidden node:  6.314657328664333 (+/-) 0.5016633364983542
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 6
No. of parameters : 174

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:26:42 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.85717858929465 (+/-) 1.655704660434071
Testing Loss:  0.33838523768555706 (+/-) 0.02798390451082156
Precision:  0.8401650434484994
Recall:  0.8385717858929465
F1 score:  0.8383813919271698
Testing Time:  0.007975316035741564 (+/-) 0.004470240770647941
Training Time:  0.78202047975377 (+/-) 0.021304375886472562


=== Average network evolution ===
Total hidden node:  6.0605302651325665 (+/-) 0.44602397654532705
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 6
No. of parameters : 174

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:26:43 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.94857428714357 (+/-) 1.65074504329353
Testing Loss:  0.33611741885654683 (+/-) 0.028034849815113155
Precision:  0.8411678174663348
Recall:  0.8394857428714357
F1 score:  0.8392864810134615
Testing Time:  0.007847910227925853 (+/-) 0.004792904192226627
Training Time:  0.7828082897592747 (+/-) 0.02103366472386999


=== Average network evolution ===
Total hidden node:  4.540770385192596 (+/-) 0.507289146288754
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 4
No. of parameters : 116

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:26:36 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.41745872936468 (+/-) 2.059284018902648
Testing Loss:  0.34196616441622685 (+/-) 0.03507304970696744
Precision:  0.8361367443950527
Recall:  0.8341745872936468
F1 score:  0.8339309098923363
Testing Time:  0.00787491426281836 (+/-) 0.004789848638469876
Training Time:  0.7789903481403788 (+/-) 0.015685693785282055


=== Average network evolution ===
Total hidden node:  4.888444222111056 (+/-) 0.4540535354690224
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 5
No. of parameters : 145

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:26:45 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.84632316158078 (+/-) 1.6779520252731805
Testing Loss:  0.33673544112535164 (+/-) 0.027558521239185865
Precision:  0.8402687536777249
Recall:  0.8384632316158079
F1 score:  0.8382474381771258
Testing Time:  0.00798410949497118 (+/-) 0.004686535478036499
Training Time:  0.7836397167680978 (+/-) 0.02311818163678738


=== Average network evolution ===
Total hidden node:  6.065532766383192 (+/-) 0.726272221327263
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 5
No. of parameters : 145

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  83.84343343343342
Std Accuracy:  1.6752297806991925
Hidden Node mean 5.574474474474474
Hidden Node std:  0.8948798293671095
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% Data
100% (2000 of 2000) |####################| Elapsed Time: 0:14:12 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.47398699349675 (+/-) 2.499399359928525
Testing Loss:  0.3441110202942925 (+/-) 0.03773954841893645
Precision:  0.836080298928544
Recall:  0.8347398699349675
F1 score:  0.8345738307554551
Testing Time:  0.007979063703871895 (+/-) 0.004515432249529046
Training Time:  0.4070171101681288 (+/-) 0.013130394114696834


=== Average network evolution ===
Total hidden node:  5.179589794897448 (+/-) 0.4531736035708553
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 5
No. of parameters : 145

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:14:12 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.53186593296648 (+/-) 2.0733184499088373
Testing Loss:  0.3437170646767905 (+/-) 0.03408279775211389
Precision:  0.8367160011872478
Recall:  0.8353186593296649
F1 score:  0.835146518768847
Testing Time:  0.008240557480240059 (+/-) 0.004648430910990265
Training Time:  0.40652117555054385 (+/-) 0.01189760135514526


=== Average network evolution ===
Total hidden node:  7.039019509754877 (+/-) 0.19364138920532062
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 7
No. of parameters : 203

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:14:14 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.51310655327664 (+/-) 2.0845601453668596
Testing Loss:  0.3438651190959793 (+/-) 0.03423983403802886
Precision:  0.8364595259580453
Recall:  0.8351310655327664
F1 score:  0.8349670803803341
Testing Time:  0.008017756331855502 (+/-) 0.004690688222398447
Training Time:  0.40793715303334194 (+/-) 0.01452082886345455


=== Average network evolution ===
Total hidden node:  5.992496248124062 (+/-) 0.13210771394953794
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 6
No. of parameters : 174

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:14:14 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.42786393196597 (+/-) 1.804612784822916
Testing Loss:  0.34438036775040354 (+/-) 0.03297467971465163
Precision:  0.8360139789263715
Recall:  0.8342786393196598
F1 score:  0.8340631485023473
Testing Time:  0.0076815375928702264 (+/-) 0.00468026620814667
Training Time:  0.40795423496717687 (+/-) 0.015023772617767375


=== Average network evolution ===
Total hidden node:  3.372186093046523 (+/-) 0.6765912068725857
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 4
No. of parameters : 116

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:14:19 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.47393696848425 (+/-) 2.167656666942873
Testing Loss:  0.3458197766420184 (+/-) 0.03564322691972024
Precision:  0.8359443878998468
Recall:  0.8347393696848424
F1 score:  0.8345900042082209
Testing Time:  0.00797703410459197 (+/-) 0.004787995842104333
Training Time:  0.4101819817932801 (+/-) 0.01459903164948159


=== Average network evolution ===
Total hidden node:  4.6968484242121065 (+/-) 0.5747262197343124
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 5
No. of parameters : 145

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  83.49955955955957
Std Accuracy:  2.023120565618677
Hidden Node mean 5.256256256256257
Hidden Node std:  1.3142776434690353
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
infinite delay
100% (2000 of 2000) |####################| Elapsed Time: 0:00:37 ETA:  00:00:00

=== Performance result ===
Accuracy:  69.91825912956477 (+/-) 1.4685571493598437
Testing Loss:  0.6425071584397164 (+/-) 0.003226252701281
Precision:  0.7258352718462668
Recall:  0.6991825912956479
F1 score:  0.6900518192228281
Testing Time:  0.007195837560923711 (+/-) 0.004749730449307384
Training Time:  9.973267425949303e-07 (+/-) 3.151457822259044e-05


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 4
No. of parameters : 116

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:37 ETA:  00:00:00

=== Performance result ===
Accuracy:  62.393896948474236 (+/-) 1.5286042937690438
Testing Loss:  0.6590038358777568 (+/-) 0.0038654162989762027
Precision:  0.6468573262767628
Recall:  0.6239389694847424
F1 score:  0.6086421519551175
Testing Time:  0.007115222860301 (+/-) 0.004801325691911151
Training Time:  4.985441023734523e-07 (+/-) 2.2284419480405576e-05


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 3
No. of parameters : 87

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:37 ETA:  00:00:00

=== Performance result ===
Accuracy:  58.016758379189596 (+/-) 1.555604142856791
Testing Loss:  0.6851458680098984 (+/-) 0.0017240404671653093
Precision:  0.5962084719516195
Recall:  0.5801675837918959
F1 score:  0.561858701726377
Testing Time:  0.007289949687139102 (+/-) 0.005053250819275446
Training Time:  2.0082501425273183e-06 (+/-) 4.485227638118325e-05


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 3
No. of parameters : 87

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
 99% (1999 of 2000) |################### | Elapsed Time: 0:00:39 ETA:   0:00:00

=== Performance result ===
Accuracy:  50.22846423211605 (+/-) 1.6139633302562995
Testing Loss:  0.681609387812822 (+/-) 0.007060664959630253
Precision:  0.7486344508425445
Recall:  0.5022846423211605
F1 score:  0.33831337782054066
Testing Time:  0.007450149797570294 (+/-) 0.0048296605113526715
Training Time:  1.4971828031325232e-06 (+/-) 3.861842050085102e-05


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 3
No. of parameters : 87

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:37 ETA:  00:00:00

=== Performance result ===
Accuracy:  64.38759379689844 (+/-) 1.5414682044058314
Testing Loss:  0.6761737258330531 (+/-) 0.0015618760469291468
Precision:  0.6448363496820557
Recall:  0.6438759379689845
F1 score:  0.6432791386246188
Testing Time:  0.007348885948864325 (+/-) 0.004776797935159196
Training Time:  9.978038182909815e-07 (+/-) 3.1529656969174065e-05


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=28, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 28
No. of nodes : 3
No. of parameters : 87

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  60.98926926926927
Std Accuracy:  6.778579505078718
Hidden Node mean 3.2
Hidden Node std:  0.4
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [8]:
%run NADINE_classification_susy.ipynb
Number of input:  18
Number of output:  2
Number of batch:  2000
All Data
100% (2000 of 2000) |####################| Elapsed Time: 0:51:42 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.95457728864433 (+/-) 2.5271389008949745
Testing Loss:  0.46798808101178885 (+/-) 0.03389240952010673
Precision:  0.7816274925294973
Recall:  0.7795457728864432
F1 score:  0.7774906867118454
Testing Time:  0.008336776491998136 (+/-) 0.004408220289888391
Training Time:  1.531872933479832 (+/-) 0.03883922979344989


=== Average network evolution ===
Total hidden node:  10.646823411705853 (+/-) 1.9365442845995733
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 12
No. of parameters : 228

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 12
No. of output : 2
No. of parameters : 26
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:51:41 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.08119059529764 (+/-) 2.5774091452741192
Testing Loss:  0.4665262839417269 (+/-) 0.03355245523942899
Precision:  0.7829252115003457
Recall:  0.7808119059529764
F1 score:  0.7787698326157988
Testing Time:  0.008957786521892538 (+/-) 0.004780694020687537
Training Time:  1.5309998615793492 (+/-) 0.029274536249909736


=== Average network evolution ===
Total hidden node:  18.209104552276138 (+/-) 2.175008616032174
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=20, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 20
No. of parameters : 380

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=20, out_features=2, bias=True)
)
No. of inputs : 20
No. of output : 2
No. of parameters : 42
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:51:28 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.73356678339171 (+/-) 2.7136508221968447
Testing Loss:  0.4739224623148891 (+/-) 0.0357267123254208
Precision:  0.7794798124049296
Recall:  0.7773356678339169
F1 score:  0.7752049513975948
Testing Time:  0.008148812007283854 (+/-) 0.004738067416356157
Training Time:  1.5253643019906635 (+/-) 0.03378511922916777


=== Average network evolution ===
Total hidden node:  8.349174587293646 (+/-) 2.15510099512392
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=10, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 10
No. of parameters : 190

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 10
No. of output : 2
No. of parameters : 22
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:51:40 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.77358679339669 (+/-) 2.72511941559637
Testing Loss:  0.4718902396553454 (+/-) 0.036043795504685075
Precision:  0.77929750681394
Recall:  0.777735867933967
F1 score:  0.775883359376995
Testing Time:  0.008288919597700155 (+/-) 0.004707945583703868
Training Time:  1.5312942700006773 (+/-) 0.03239534576375941


=== Average network evolution ===
Total hidden node:  10.02751375687844 (+/-) 2.284152369635112
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 12
No. of parameters : 228

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 12
No. of output : 2
No. of parameters : 26
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:51:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.61080540270135 (+/-) 2.8062110954108257
Testing Loss:  0.4758321739930758 (+/-) 0.03581825398119729
Precision:  0.7796128835697405
Recall:  0.7761080540270135
F1 score:  0.7733772661883147
Testing Time:  0.007715155805212787 (+/-) 0.00491106841488459
Training Time:  1.5273227577152224 (+/-) 0.024552974930873876


=== Average network evolution ===
Total hidden node:  4.882941470735368 (+/-) 1.828927729681038
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 6
No. of parameters : 114

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  77.8421121121121
Std Accuracy:  2.6280654618016954
Hidden Node mean 10.425225225225224
Hidden Node std:  4.847541953125485
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% Data
100% (2000 of 2000) |####################| Elapsed Time: 0:26:40 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.38724362181091 (+/-) 2.983360953265096
Testing Loss:  0.47770371718547416 (+/-) 0.03861482503176204
Precision:  0.7762272777490693
Recall:  0.7738724362181091
F1 score:  0.7715685100925419
Testing Time:  0.008478721777995626 (+/-) 0.004863724498605859
Training Time:  0.7805007695555389 (+/-) 0.022941870370947432


=== Average network evolution ===
Total hidden node:  11.347673836918458 (+/-) 2.416320062200439
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=14, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 14
No. of parameters : 266

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=14, out_features=2, bias=True)
)
No. of inputs : 14
No. of output : 2
No. of parameters : 30
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:26:27 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.06058029014507 (+/-) 3.1176749047708032
Testing Loss:  0.4833156440692165 (+/-) 0.03914441432304305
Precision:  0.7725210850586511
Recall:  0.7706058029014508
F1 score:  0.7684303150450307
Testing Time:  0.007840401533545703 (+/-) 0.004743831069515042
Training Time:  0.7747281516057483 (+/-) 0.015713147162417104


=== Average network evolution ===
Total hidden node:  6.172586293146574 (+/-) 2.1288482477300135
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 9
No. of parameters : 171

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 9
No. of output : 2
No. of parameters : 20
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:26:35 ETA:  00:00:00

=== Performance result ===
Accuracy:  76.64967483741871 (+/-) 4.192609883251568
Testing Loss:  0.48823821927798633 (+/-) 0.04787508510173317
Precision:  0.7693834489704106
Recall:  0.7664967483741871
F1 score:  0.7637658827575643
Testing Time:  0.008113261042027666 (+/-) 0.004872810113813746
Training Time:  0.7783785801401372 (+/-) 0.021566474492765216


=== Average network evolution ===
Total hidden node:  6.6368184092046025 (+/-) 2.188277715972628
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 9
No. of parameters : 171

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 9
No. of output : 2
No. of parameters : 20
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:26:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  76.93026513256629 (+/-) 3.600507090035036
Testing Loss:  0.4850121295529643 (+/-) 0.0438590268283625
Precision:  0.7728522333714393
Recall:  0.7693026513256628
F1 score:  0.7663638575201127
Testing Time:  0.007722694316823939 (+/-) 0.004940909900269146
Training Time:  0.7763779255197667 (+/-) 0.018430621292625567


=== Average network evolution ===
Total hidden node:  5.182591295647824 (+/-) 1.9670057560582925
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 8
No. of parameters : 152

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:26:32 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.23876938469236 (+/-) 3.387905198963182
Testing Loss:  0.47993546693011363 (+/-) 0.04092340486534516
Precision:  0.7741033372285039
Recall:  0.7723876938469234
F1 score:  0.7703491938942769
Testing Time:  0.008302591394459742 (+/-) 0.004655867523796111
Training Time:  0.7770098834827341 (+/-) 0.018997835725836965


=== Average network evolution ===
Total hidden node:  10.449224612306153 (+/-) 2.4230628893938175
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=13, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 13
No. of parameters : 247

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 13
No. of output : 2
No. of parameters : 28
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  77.06343343343343
Std Accuracy:  3.4630370845794443
Hidden Node mean 7.95965965965966
Hidden Node std:  3.323178870010817
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% Data
100% (2000 of 2000) |####################| Elapsed Time: 0:14:07 ETA:  00:00:00

=== Performance result ===
Accuracy:  74.45577788894447 (+/-) 5.171462790776855
Testing Loss:  0.5183526440344196 (+/-) 0.056589860443886884
Precision:  0.7487126942721679
Recall:  0.7445577788894447
F1 score:  0.7404975105007559
Testing Time:  0.006692481315273115 (+/-) 0.003870272721716512
Training Time:  0.4058427988379642 (+/-) 0.020509090982460655


=== Average network evolution ===
Total hidden node:  2.8099049524762383 (+/-) 0.7184288489307575
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 4
No. of parameters : 76

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:14:06 ETA:  00:00:00

=== Performance result ===
Accuracy:  76.06213106553277 (+/-) 3.636414912731932
Testing Loss:  0.4964904447774281 (+/-) 0.04589634448868197
Precision:  0.7632474861248275
Recall:  0.7606213106553277
F1 score:  0.7578476213814805
Testing Time:  0.008248140002084172 (+/-) 0.0045581033909403434
Training Time:  0.40368189449129016 (+/-) 0.011363983131302597


=== Average network evolution ===
Total hidden node:  7.67183591795898 (+/-) 1.6120364486718248
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=10, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 10
No. of parameters : 190

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 10
No. of output : 2
No. of parameters : 22
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:14:01 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.2928464232116 (+/-) 4.008896291582547
Testing Loss:  0.5105746760405321 (+/-) 0.047364764191401024
Precision:  0.7571111846534918
Recall:  0.752928464232116
F1 score:  0.74917750993114
Testing Time:  0.00743556797892049 (+/-) 0.004566097001783393
Training Time:  0.40202717783452274 (+/-) 0.011475572168860675


=== Average network evolution ===
Total hidden node:  3.5602801400700352 (+/-) 0.9298792713521609
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 5
No. of parameters : 95

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:14:07 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.7591795897949 (+/-) 4.217196568896687
Testing Loss:  0.5010643777190118 (+/-) 0.048502255333986555
Precision:  0.7593131910056503
Recall:  0.757591795897949
F1 score:  0.7552004279454524
Testing Time:  0.007932956603958107 (+/-) 0.004618080317077822
Training Time:  0.4044680723015698 (+/-) 0.015074947785046549


=== Average network evolution ===
Total hidden node:  6.427213606803401 (+/-) 1.4503901563709665
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 9
No. of parameters : 171

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 9
No. of output : 2
No. of parameters : 20
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:14:00 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.06438219109555 (+/-) 5.067929948508019
Testing Loss:  0.5097242912958717 (+/-) 0.052509488367184984
Precision:  0.7543043343139796
Recall:  0.7506438219109555
F1 score:  0.7470539921002298
Testing Time:  0.006962647492913022 (+/-) 0.004383670312037916
Training Time:  0.40201500608302043 (+/-) 0.011512957053012264


=== Average network evolution ===
Total hidden node:  3.011505752876438 (+/-) 0.6705165770374076
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 4
No. of parameters : 76

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  75.33613613613613
Std Accuracy:  4.477766170241681
Hidden Node mean 4.6967967967967965
Hidden Node std:  2.283697664922948
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 99% (1999 of 2000) |################### | Elapsed Time: 0:00:38 ETA:   0:00:00

=== Performance result ===
Accuracy:  54.238919459729864 (+/-) 1.5656377754252186
Testing Loss:  0.6961198691847087 (+/-) 0.006240284642032272
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.2941860384159063
Recall:  0.5423891945972986
F1 score:  0.3814679711792393
Testing Time:  0.00744759446087332 (+/-) 0.004837734713018316
Training Time:  4.980670266774012e-07 (+/-) 2.226309467707506e-05


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 6
No. of parameters : 114

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
 99% (1999 of 2000) |################### | Elapsed Time: 0:00:37 ETA:   0:00:00

=== Performance result ===
Accuracy:  56.175337668834416 (+/-) 1.5605879632152218
Testing Loss:  0.6751180544324134 (+/-) 0.00509114181611653
Precision:  0.7132905022374537
Recall:  0.5617533766883441
F1 score:  0.42707030865874057
Testing Time:  0.007155278135741455 (+/-) 0.00470282663310498
Training Time:  1.4965864585124594e-06 (+/-) 3.860303540900367e-05


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 4
No. of parameters : 76

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:39 ETA:  00:00:00

=== Performance result ===
Accuracy:  54.31745872936469 (+/-) 1.5635833017602094
Testing Loss:  0.6902488746781419 (+/-) 0.006759512549503921
Precision:  0.6900003491437942
Recall:  0.5431745872936469
F1 score:  0.38349040597078354
Testing Time:  0.007570054186410222 (+/-) 0.0049033655642956985
Training Time:  4.996175226895675e-07 (+/-) 2.23324002878993e-05


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 3
No. of parameters : 57

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  54.53011505752877 (+/-) 1.5618300782623784
Testing Loss:  0.6860506683066226 (+/-) 0.0017765962104314317
Precision:  0.6997897003148242
Recall:  0.5453011505752876
F1 score:  0.38874273360211703
Testing Time:  0.007305128088946817 (+/-) 0.004742141532270598
Training Time:  1.4961093828164082e-06 (+/-) 3.859072969178409e-05


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 4
No. of parameters : 76

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  54.238919459729864 (+/-) 1.5656377754252186
Testing Loss:  0.702019528933559 (+/-) 0.008200431371711172
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.2941860384159063
Recall:  0.5423891945972986
F1 score:  0.3814679711792393
Testing Time:  0.007169294500422514 (+/-) 0.004712738245836063
Training Time:  4.98782640221478e-07 (+/-) 2.229508188207085e-05


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=18, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 18
No. of nodes : 4
No. of parameters : 76

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  54.69925925925926
Std Accuracy:  1.731756458710097
Hidden Node mean 4.2
Hidden Node std:  0.9797958971132714
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [9]:
%run NADINE_classification_electricitypricing.ipynb
Number of input:  8
Number of output:  2
Number of batch:  45
All Data
100% (45 of 45) |########################| Elapsed Time: 0:02:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  56.088636363636354 (+/-) 7.873436811926857
Testing Loss:  0.6900654421611265 (+/-) 0.021927531906216182
Precision:  0.5292060331246423
Recall:  0.5608863636363637
F1 score:  0.5109524497303286
Testing Time:  0.004230076616460627 (+/-) 0.005702403309514926
Training Time:  3.123543219132857 (+/-) 3.468867672182119


=== Average network evolution ===
Total hidden node:  15.840909090909092 (+/-) 6.349456604588593
Number of layer:  3.5454545454545454 (+/-) 0.9875254992000196


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 3
No. of parameters : 27

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 4
No. of parameters : 16

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 6
No. of parameters : 30

 4 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 7
No. of parameters : 49

 5 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 5
No. of parameters : 40

 6 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001, 0.0001, 0.0001]
100% (45 of 45) |########################| Elapsed Time: 0:01:43 ETA:  00:00:00

=== Performance result ===
Accuracy:  61.5 (+/-) 7.610369719844579
Testing Loss:  0.6423078761859373 (+/-) 0.041741273061774685
Precision:  0.6054952803224337
Recall:  0.615
F1 score:  0.5984579458875032
Testing Time:  0.0024590275504372335 (+/-) 0.0009631354212277176
Training Time:  2.3424539999528364 (+/-) 2.0055707400230265


=== Average network evolution ===
Total hidden node:  9.136363636363637 (+/-) 5.12831227372705
Number of layer:  2.1363636363636362 (+/-) 1.198311484224006


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 4
No. of parameters : 36

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 3
No. of parameters : 15

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 4
No. of parameters : 16

 4 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 7
No. of parameters : 35

 5 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001, 0.0001]
100% (45 of 45) |########################| Elapsed Time: 0:01:32 ETA:  00:00:00

=== Performance result ===
Accuracy:  56.929545454545455 (+/-) 6.475498560849987
Testing Loss:  0.6847799542275342 (+/-) 0.026461256559532322
Precision:  0.5414415818550379
Recall:  0.5692954545454545
F1 score:  0.5185883634912459
Testing Time:  0.002593230117451061 (+/-) 0.0007136240333021079
Training Time:  2.087898514487527 (+/-) 1.230420982387765


=== Average network evolution ===
Total hidden node:  9.727272727272727 (+/-) 1.8508430065002117
Number of layer:  2.159090909090909 (+/-) 0.6008774575637402


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 63

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=2, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 2
No. of parameters : 16

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=2, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 2
No. of nodes : 4
No. of parameters : 12

 4 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001]
100% (45 of 45) |########################| Elapsed Time: 0:01:51 ETA:  00:00:00

=== Performance result ===
Accuracy:  58.41136363636363 (+/-) 6.74985575297357
Testing Loss:  0.6794444512237202 (+/-) 0.026962036030643524
Precision:  0.5659518657319237
Recall:  0.5841136363636363
F1 score:  0.5489847390526132
Testing Time:  0.002870180390097878 (+/-) 0.0008821189022379835
Training Time:  2.5197523073716597 (+/-) 2.299057321611756


=== Average network evolution ===
Total hidden node:  11.431818181818182 (+/-) 5.210992434149212
Number of layer:  2.5454545454545454 (+/-) 0.9875254992000196


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 45

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 4
No. of parameters : 24

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 3
No. of parameters : 15

 4 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 8
No. of parameters : 32

 5 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001, 0.0001]
100% (45 of 45) |########################| Elapsed Time: 0:01:13 ETA:  00:00:00

=== Performance result ===
Accuracy:  61.945454545454545 (+/-) 7.774213042472664
Testing Loss:  0.6325082670558583 (+/-) 0.05834837343200157
Precision:  0.6111065420140294
Recall:  0.6194545454545455
F1 score:  0.607888904184372
Testing Time:  0.002092578194358132 (+/-) 0.0006768741018981584
Training Time:  1.6607387878678062 (+/-) 0.5026806543196276


=== Average network evolution ===
Total hidden node:  7.704545454545454 (+/-) 3.0568815471634534
Number of layer:  1.25 (+/-) 0.4330127018922193


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 63

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 6
No. of parameters : 48

 3 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001]
Mean Accuracy:  59.02976744186047
Std Accuracy:  7.511024297153375
Hidden Node mean 10.874418604651163
Hidden Node std:  5.416430345276791
Hidden Layer mean:  2.353488372093023
Hidden Layer std:  1.1556431486726748
50% Data
100% (45 of 45) |########################| Elapsed Time: 0:00:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  62.69318181818183 (+/-) 6.89648783233086
Testing Loss:  0.6258891265500676 (+/-) 0.04981666813935529
Precision:  0.6203247289864602
Recall:  0.6269318181818182
F1 score:  0.6197309257123984
Testing Time:  0.0017736229029568758 (+/-) 0.000455783268869294
Training Time:  0.7680414048108187 (+/-) 0.0127041853020773


=== Average network evolution ===
Total hidden node:  5.9772727272727275 (+/-) 0.14903269373413638
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 54

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
100% (45 of 45) |########################| Elapsed Time: 0:00:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  60.75454545454544 (+/-) 7.98602343146662
Testing Loss:  0.6463055244900964 (+/-) 0.05594611827292049
Precision:  0.598994370683884
Recall:  0.6075454545454545
F1 score:  0.5980861796639305
Testing Time:  0.0018613934516906738 (+/-) 0.00048738213544072256
Training Time:  0.7625350085171786 (+/-) 0.016469567349510723


=== Average network evolution ===
Total hidden node:  6.545454545454546 (+/-) 0.49792959773196915
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 63

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (45 of 45) |########################| Elapsed Time: 0:00:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  64.00227272727273 (+/-) 7.8633640914503475
Testing Loss:  0.6203348223458637 (+/-) 0.059221981054932225
Precision:  0.6363734525591423
Recall:  0.6400227272727272
F1 score:  0.6372628837230548
Testing Time:  0.0017918727614662864 (+/-) 0.00038235176300413146
Training Time:  0.7649643204428933 (+/-) 0.013774717020110839


=== Average network evolution ===
Total hidden node:  5.545454545454546 (+/-) 0.49792959773196915
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 54

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
100% (45 of 45) |########################| Elapsed Time: 0:00:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  61.415909090909096 (+/-) 6.593727845523081
Testing Loss:  0.6425982469862158 (+/-) 0.04851161347292417
Precision:  0.6044252337949235
Recall:  0.6141590909090909
F1 score:  0.5933801247284796
Testing Time:  0.0017538612539117987 (+/-) 0.00041422620359300206
Training Time:  0.7647274082357233 (+/-) 0.011661524538827595


=== Average network evolution ===
Total hidden node:  5.545454545454546 (+/-) 0.49792959773196915
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 54

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
100% (45 of 45) |########################| Elapsed Time: 0:00:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  60.57727272727273 (+/-) 7.214816309523431
Testing Loss:  0.6459532434290106 (+/-) 0.054744189003492226
Precision:  0.5944299435314258
Recall:  0.6057727272727272
F1 score:  0.5838548310389271
Testing Time:  0.0018204342235218394 (+/-) 0.0004188041145837858
Training Time:  0.7599805918606845 (+/-) 0.011473545585987729


=== Average network evolution ===
Total hidden node:  7.045454545454546 (+/-) 0.20829889522526543
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 63

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  62.26883720930232
Std Accuracy:  6.701010409511607
Hidden Node mean 6.1395348837209305
Hidden Node std:  0.7081385802109118
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% Data
100% (45 of 45) |########################| Elapsed Time: 0:00:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  61.70454545454545 (+/-) 6.525542629664621
Testing Loss:  0.6409691951491616 (+/-) 0.03997752527110094
Precision:  0.6077891832596168
Recall:  0.6170454545454546
F1 score:  0.5995189252476162
Testing Time:  0.0018435554070906205 (+/-) 0.0004977706414413864
Training Time:  0.40245831554586237 (+/-) 0.011997636735961159


=== Average network evolution ===
Total hidden node:  4.7727272727272725 (+/-) 0.4701854742176637
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 45

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (45 of 45) |########################| Elapsed Time: 0:00:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  58.80227272727273 (+/-) 6.416438003510402
Testing Loss:  0.6626005118543451 (+/-) 0.035105224101239625
Precision:  0.5718388536749407
Recall:  0.5880227272727273
F1 score:  0.5581361538698065
Testing Time:  0.0016653266820040617 (+/-) 0.000508609999709917
Training Time:  0.4031171744520014 (+/-) 0.013154746109783004


=== Average network evolution ===
Total hidden node:  4.545454545454546 (+/-) 0.49792959773196915
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 45

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (45 of 45) |########################| Elapsed Time: 0:00:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  61.775 (+/-) 6.897039055078109
Testing Loss:  0.6418313722718846 (+/-) 0.044070089256331144
Precision:  0.6089288449330501
Recall:  0.61775
F1 score:  0.6044201063818361
Testing Time:  0.0019264492121609774 (+/-) 0.0004233096868348012
Training Time:  0.39600146358663385 (+/-) 0.009266562601823224


=== Average network evolution ===
Total hidden node:  6.590909090909091 (+/-) 0.4916660830178168
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 63

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (45 of 45) |########################| Elapsed Time: 0:00:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  58.963636363636354 (+/-) 6.457451330513488
Testing Loss:  0.6619926961985502 (+/-) 0.03642827155706012
Precision:  0.5737830731039351
Recall:  0.5896363636363636
F1 score:  0.55902165301373
Testing Time:  0.0017501657659357245 (+/-) 0.00041480783674129913
Training Time:  0.39625219865278766 (+/-) 0.008553493608402072


=== Average network evolution ===
Total hidden node:  4.5 (+/-) 0.5
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 45

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (45 of 45) |########################| Elapsed Time: 0:00:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  59.2 (+/-) 7.5700966728545565
Testing Loss:  0.6524218361486088 (+/-) 0.038175686018998296
Precision:  0.5787635492117729
Recall:  0.592
F1 score:  0.5727082914759181
Testing Time:  0.001841138709675182 (+/-) 0.0005011335921089303
Training Time:  0.3974649581042203 (+/-) 0.013749878369493908


=== Average network evolution ===
Total hidden node:  5.4772727272727275 (+/-) 0.6211659258507477
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 54

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  60.153488372093015
Std Accuracy:  6.5577062367167755
Hidden Node mean 5.176744186046512
Hidden Node std:  0.938290679733898
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 88% (40 of 45) |#####################   | Elapsed Time: 0:00:00 ETA:  00:00:00

=== Performance result ===
Accuracy:  57.4840909090909 (+/-) 6.357384510151746
Testing Loss:  0.6895422718741677 (+/-) 0.006026430354996066
Precision:  0.5449361382569123
Recall:  0.5748409090909091
F1 score:  0.5002452441554814
Testing Time:  0.0009053349494934082 (+/-) 0.00041547511089249164
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 45

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
100% (45 of 45) |########################| Elapsed Time: 0:00:00 ETA:  00:00:00

=== Performance result ===
Accuracy:  43.56363636363636 (+/-) 7.04392358732917
Testing Loss:  0.7470100210471586 (+/-) 0.04702806080860336
Precision:  0.6122641980725461
Recall:  0.43563636363636365
F1 score:  0.290055142646441
Testing Time:  0.0008377703753384677 (+/-) 0.0004206037549611421
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 3
No. of parameters : 27

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
 95% (43 of 45) |######################  | Elapsed Time: 0:00:00 ETA:  00:00:00

=== Performance result ===
Accuracy:  57.75909090909091 (+/-) 6.413420523396026
Testing Loss:  0.6821321939880197 (+/-) 0.02238828870737873
Precision:  0.48578461625923824
Recall:  0.577590909090909
F1 score:  0.42342251171979395
Testing Time:  0.0008845546028830788 (+/-) 0.0003815061135243028
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 4
No. of parameters : 36

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
 86% (39 of 45) |####################    | Elapsed Time: 0:00:00 ETA:  00:00:00

=== Performance result ===
Accuracy:  52.24772727272725 (+/-) 5.4648250673309695
Testing Loss:  0.6910624761473049 (+/-) 0.005688135580340763
Precision:  0.5263060247681588
Recall:  0.5224772727272727
F1 score:  0.5240679518098668
Testing Time:  0.0009294314817948775 (+/-) 0.0005795397471903198
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 4
No. of parameters : 36

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
N/A% (0 of 45) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--

=== Performance result ===
Accuracy:  48.13863636363636 (+/-) 7.012528266320958
Testing Loss:  0.6980154256929051 (+/-) 0.014251611261498246
Precision:  0.5814207155948179
Recall:  0.4813863636363636
F1 score:  0.42104715934853
Testing Time:  0.0007011944597417659 (+/-) 0.0005004274457939448
Training Time:  2.2666020826859906e-05 (+/-) 0.00014863103816268263


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 3
No. of parameters : 27

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
Mean Accuracy:  51.712093023255804
Std Accuracy:  8.201408999235559
Hidden Node mean 3.8
Hidden Node std:  0.7483314773547883
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [1]:
%run NADINE_classification_occupancy.ipynb
Number of input:  5
Number of output:  2
Number of batch:  20
All Data
100% (20 of 20) |########################| Elapsed Time: 0:01:54 ETA:  00:00:00

=== Performance result ===
Accuracy:  66.11578947368422 (+/-) 30.047370725816027
Testing Loss:  0.7413052906723399 (+/-) 0.6841498508550296
Precision:  0.6181622192982457
Recall:  0.6611578947368421
F1 score:  0.6374759268636577
Testing Time:  0.0067740239595112045 (+/-) 0.007969442062941503
Training Time:  6.031864680741963 (+/-) 4.475899293238954


=== Average network evolution ===
Total hidden node:  37.578947368421055 (+/-) 16.432688185358778
Number of layer:  6.2631578947368425 (+/-) 2.935124946020776


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 7
No. of parameters : 42

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 4
No. of parameters : 32

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 9
No. of parameters : 45

 4 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=9, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 9
No. of nodes : 7
No. of parameters : 70

 5 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 6
No. of parameters : 48

 6 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 5
No. of parameters : 35

 7 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 7
No. of parameters : 42

 8 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 4
No. of parameters : 32

 9 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 25

 10 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 4
No. of parameters : 24

 11 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 3
No. of parameters : 15

 12 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.02, 0.02]
100% (20 of 20) |########################| Elapsed Time: 0:01:39 ETA:  00:00:00

=== Performance result ===
Accuracy:  71.83157894736841 (+/-) 26.190557813066082
Testing Loss:  0.7240843404893225 (+/-) 0.7491465573109074
Precision:  0.5845494269005848
Recall:  0.7183157894736842
F1 score:  0.6445656508337739
Testing Time:  0.008803857000250565 (+/-) 0.010305741310137373
Training Time:  5.204964411886115 (+/-) 3.117274289647815


=== Average network evolution ===
Total hidden node:  45.73684210526316 (+/-) 19.401145124269444
Number of layer:  6.157894736842105 (+/-) 2.7959210495354583


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 7
No. of parameters : 42

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 7
No. of parameters : 56

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 12
No. of parameters : 96

 4 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=12, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 12
No. of nodes : 5
No. of parameters : 65

 5 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 8
No. of parameters : 48

 6 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 63

 7 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 6
No. of parameters : 48

 8 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 6
No. of parameters : 42

 9 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 9
No. of parameters : 63

 10 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=9, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 9
No. of nodes : 6
No. of parameters : 60

 11 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.02]
100% (20 of 20) |########################| Elapsed Time: 0:01:52 ETA:  00:00:00

=== Performance result ===
Accuracy:  68.9578947368421 (+/-) 28.34010206476948
Testing Loss:  0.7092076442449501 (+/-) 0.6989813017572891
Precision:  0.6098767306501547
Recall:  0.6895789473684211
F1 score:  0.6436429043085233
Testing Time:  0.006817290657445004 (+/-) 0.009348207849188047
Training Time:  5.91120171546936 (+/-) 3.8944360573062093


=== Average network evolution ===
Total hidden node:  35.73684210526316 (+/-) 15.592987983007848
Number of layer:  6.2631578947368425 (+/-) 2.935124946020776


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 5
No. of parameters : 30

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 5
No. of parameters : 30

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=10, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 10
No. of parameters : 60

 4 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=10, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 10
No. of nodes : 6
No. of parameters : 66

 5 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 6
No. of parameters : 42

 6 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 4
No. of parameters : 28

 7 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 25

 8 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 4
No. of parameters : 24

 9 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 4
No. of parameters : 20

 10 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 7
No. of parameters : 35

 11 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 7
No. of parameters : 56

 12 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.02, 0.02]
100% (20 of 20) |########################| Elapsed Time: 0:01:42 ETA:  00:00:00

=== Performance result ===
Accuracy:  66.0263157894737 (+/-) 29.97484633392442
Testing Loss:  0.7211671401746571 (+/-) 0.6458575280478268
Precision:  0.633785830406839
Recall:  0.6602631578947369
F1 score:  0.6459800711196622
Testing Time:  0.005980027349371659 (+/-) 0.009533765487810101
Training Time:  5.392461889668515 (+/-) 4.421539000421144


=== Average network evolution ===
Total hidden node:  28.36842105263158 (+/-) 14.970885873010811
Number of layer:  5.0 (+/-) 2.809757434745082


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 8
No. of parameters : 48

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 54

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 4
No. of parameters : 28

 4 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 25

 5 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 7
No. of parameters : 42

 6 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 7
No. of parameters : 56

 7 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 3
No. of parameters : 24

 8 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 5
No. of parameters : 20

 9 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 4
No. of parameters : 24

 10 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 3
No. of parameters : 15

 11 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.02, 0.02]
100% (20 of 20) |########################| Elapsed Time: 0:01:45 ETA:  00:00:00

=== Performance result ===
Accuracy:  72.44210526315791 (+/-) 23.262604184884456
Testing Loss:  0.730884123299467 (+/-) 0.7203345006255195
Precision:  0.6297510687158236
Recall:  0.724421052631579
F1 score:  0.6642873264134467
Testing Time:  0.007869143235056024 (+/-) 0.009911177341382168
Training Time:  5.54103392048886 (+/-) 3.8335136247810304


=== Average network evolution ===
Total hidden node:  45.78947368421053 (+/-) 19.294337722348605
Number of layer:  6.2631578947368425 (+/-) 2.935124946020776


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 9
No. of parameters : 54

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=9, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 9
No. of nodes : 4
No. of parameters : 40

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=13, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 13
No. of parameters : 65

 4 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=13, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 13
No. of nodes : 6
No. of parameters : 84

 5 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 8
No. of parameters : 56

 6 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 54

 7 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 7
No. of parameters : 49

 8 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 7
No. of parameters : 56

 9 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 5
No. of parameters : 40

 10 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 3
No. of parameters : 18

 11 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=3, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 3
No. of nodes : 4
No. of parameters : 16

 12 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.02, 0.02]

========== Performance occupancy ==========
Preq Accuracy:  69.07 (+/-) 2.72
F1 score:  0.65 (+/-) 0.01
Precision:  0.62 (+/-) 0.02
Recall:  0.69 (+/-) 0.03
Training time:  5.62 (+/-) 0.31
Testing time:  0.01 (+/-) 0.0


========== Network ==========
Number of hidden layers:  10.6 (+/-) 0.49
Number of features:  64.2 (+/-) 7.73
50% Data
100% (20 of 20) |########################| Elapsed Time: 0:00:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.76842105263158 (+/-) 18.805963573504492
Testing Loss:  0.3486570302200945 (+/-) 0.35759483750829896
Precision:  0.827128067000654
Recall:  0.8376842105263158
F1 score:  0.8270684206800012
Testing Time:  0.00236117212395919 (+/-) 0.00048536545356259246
Training Time:  0.9028193574202689 (+/-) 0.061089705891232776


=== Average network evolution ===
Total hidden node:  10.31578947368421 (+/-) 2.3181805837416043
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=14, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 14
No. of parameters : 84

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=14, out_features=2, bias=True)
)
No. of inputs : 14
No. of output : 2
No. of parameters : 30
Dynamic laerning rate for each hidden layer:  [0.02]
100% (20 of 20) |########################| Elapsed Time: 0:00:24 ETA:  00:00:00

=== Performance result ===
Accuracy:  62.5263157894737 (+/-) 32.48698269032275
Testing Loss:  0.7588729304996761 (+/-) 0.5900879435789674
Precision:  0.6167312729617602
Recall:  0.6252631578947369
F1 score:  0.6209093120939793
Testing Time:  0.0030910592330129524 (+/-) 0.0011165618252967848
Training Time:  1.269457177111977 (+/-) 0.4350584521434636


=== Average network evolution ===
Total hidden node:  18.157894736842106 (+/-) 5.460510462881237
Number of layer:  2.210526315789474 (+/-) 0.6137844099837159


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 7
No. of parameters : 42

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=7, out_features=11, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 7
No. of nodes : 11
No. of parameters : 88

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=11, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 11
No. of nodes : 7
No. of parameters : 84

 4 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001]
100% (20 of 20) |########################| Elapsed Time: 0:00:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  81.10526315789475 (+/-) 18.33180938852092
Testing Loss:  0.4811954941894663 (+/-) 0.4929174435311406
Precision:  0.7952554222634699
Recall:  0.8110526315789474
F1 score:  0.7974025203218573
Testing Time:  0.0020365087609542044 (+/-) 0.0005069820694998896
Training Time:  0.9019824705625835 (+/-) 0.01403862773792278


=== Average network evolution ===
Total hidden node:  7.7894736842105265 (+/-) 1.9078493642517746
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=11, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 11
No. of parameters : 66

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=11, out_features=2, bias=True)
)
No. of inputs : 11
No. of output : 2
No. of parameters : 24
Dynamic laerning rate for each hidden layer:  [0.02]
100% (20 of 20) |########################| Elapsed Time: 0:00:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.54736842105265 (+/-) 21.379635377252484
Testing Loss:  0.5326100588335019 (+/-) 0.5236876285111566
Precision:  0.753117093742324
Recall:  0.7754736842105263
F1 score:  0.7594887861467363
Testing Time:  0.002092072838231137 (+/-) 0.0006377409806986178
Training Time:  0.9000320936504164 (+/-) 0.01737350351336918


=== Average network evolution ===
Total hidden node:  9.894736842105264 (+/-) 1.483052926695302
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=13, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 13
No. of parameters : 78

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 13
No. of output : 2
No. of parameters : 28
Dynamic laerning rate for each hidden layer:  [0.02]
100% (20 of 20) |########################| Elapsed Time: 0:00:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.27894736842104 (+/-) 18.43767821173087
Testing Loss:  0.431255930323938 (+/-) 0.4444738051287629
Precision:  0.8337756993794551
Recall:  0.8427894736842105
F1 score:  0.8283091559219236
Testing Time:  0.0019918366482383327 (+/-) 0.00045626979282153117
Training Time:  0.9034348663530851 (+/-) 0.012302593071830644


=== Average network evolution ===
Total hidden node:  6.473684210526316 (+/-) 1.6015920057582043
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 9
No. of parameters : 54

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 9
No. of output : 2
No. of parameters : 20
Dynamic laerning rate for each hidden layer:  [0.02]

========== Performance occupancy ==========
Preq Accuracy:  77.85 (+/-) 8.02
F1 score:  0.77 (+/-) 0.08
Precision:  0.77 (+/-) 0.08
Recall:  0.78 (+/-) 0.08
Training time:  0.98 (+/-) 0.15
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.4 (+/-) 0.8
Number of features:  14.4 (+/-) 5.57
25% Data
100% (20 of 20) |########################| Elapsed Time: 0:00:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  66.11578947368422 (+/-) 30.047370725816027
Testing Loss:  0.7770996762831744 (+/-) 0.6202127485966321
Precision:  0.6181622192982457
Recall:  0.6611578947368421
F1 score:  0.6374759268636577
Testing Time:  0.002880121532239412 (+/-) 0.0008516272331530303
Training Time:  0.5707024775053325 (+/-) 0.06804353319753645


=== Average network evolution ===
Total hidden node:  15.578947368421053 (+/-) 3.7740915877889587
Number of layer:  1.894736842105263 (+/-) 0.30689220499185793


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 6
No. of parameters : 36

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 12
No. of parameters : 84

 3 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 12
No. of output : 2
No. of parameters : 26
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001]
100% (20 of 20) |########################| Elapsed Time: 0:00:12 ETA:  00:00:00

=== Performance result ===
Accuracy:  61.78947368421054 (+/-) 33.073170789006326
Testing Loss:  0.7050064955102769 (+/-) 0.5140652787407359
Precision:  0.6124252984109287
Recall:  0.6178947368421053
F1 score:  0.6151255657920228
Testing Time:  0.0034108914827045644 (+/-) 0.0008668975462704134
Training Time:  0.6402965721331144 (+/-) 0.21095012193711943


=== Average network evolution ===
Total hidden node:  17.736842105263158 (+/-) 5.692489098185783
Number of layer:  2.210526315789474 (+/-) 0.6137844099837159


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 4
No. of parameters : 24

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=4, out_features=14, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 4
No. of nodes : 14
No. of parameters : 70

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=14, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 14
No. of nodes : 7
No. of parameters : 105

 4 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001]
100% (20 of 20) |########################| Elapsed Time: 0:00:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  64.86842105263159 (+/-) 29.091562398229545
Testing Loss:  0.7032000747950453 (+/-) 0.5998521724690425
Precision:  0.6127857725625887
Recall:  0.6486842105263158
F1 score:  0.6293163794854115
Testing Time:  0.0028353239360608554 (+/-) 0.0006679077558093613
Training Time:  0.5672339012748316 (+/-) 0.07621184706673266


=== Average network evolution ===
Total hidden node:  14.842105263157896 (+/-) 3.616674219496732
Number of layer:  1.894736842105263 (+/-) 0.30689220499185793


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 5
No. of parameters : 30

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 12
No. of parameters : 72

 3 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 12
No. of output : 2
No. of parameters : 26
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001]
100% (20 of 20) |########################| Elapsed Time: 0:00:12 ETA:  00:00:00

=== Performance result ===
Accuracy:  60.852631578947374 (+/-) 32.32311163403627
Testing Loss:  0.7280160220045793 (+/-) 0.5717919671517209
Precision:  0.5969272982456141
Recall:  0.6085263157894737
F1 score:  0.6026007831949999
Testing Time:  0.0033537463137978 (+/-) 0.0012572198346441784
Training Time:  0.6365938939546284 (+/-) 0.20844465429570497


=== Average network evolution ===
Total hidden node:  18.63157894736842 (+/-) 6.276191657163722
Number of layer:  2.210526315789474 (+/-) 0.6137844099837159


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 6
No. of parameters : 36

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 12
No. of parameters : 84

 3 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=12, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 12
No. of nodes : 9
No. of parameters : 117

 4 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 9
No. of output : 2
No. of parameters : 20
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001, 0.0001]
100% (20 of 20) |########################| Elapsed Time: 0:00:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  66.11578947368422 (+/-) 30.047370725816027
Testing Loss:  0.7305813410172337 (+/-) 0.6391395071522439
Precision:  0.6181622192982457
Recall:  0.6611578947368421
F1 score:  0.6374759268636577
Testing Time:  0.0029271778307462994 (+/-) 0.0006789698897705654
Training Time:  0.5708209840874923 (+/-) 0.057423669328512034


=== Average network evolution ===
Total hidden node:  15.631578947368421 (+/-) 3.730535820351442
Number of layer:  1.894736842105263 (+/-) 0.30689220499185793


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 6
No. of parameters : 36

 2 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=6, out_features=12, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 6
No. of nodes : 12
No. of parameters : 84

 3 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 12
No. of output : 2
No. of parameters : 26
Dynamic laerning rate for each hidden layer:  [0.0001, 0.0001]

========== Performance occupancy ==========
Preq Accuracy:  63.95 (+/-) 2.21
F1 score:  0.62 (+/-) 0.01
Precision:  0.61 (+/-) 0.01
Recall:  0.64 (+/-) 0.02
Training time:  0.6 (+/-) 0.03
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  2.4 (+/-) 0.49
Number of features:  21.0 (+/-) 4.15
Infinite Delay
N/A% (0 of 20) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.09473684210526 (+/-) 20.698753425068876
Testing Loss:  0.5331742975272631 (+/-) 0.30023566023729126
Precision:  0.5943598448753463
Recall:  0.7709473684210526
F1 score:  0.6712337763095327
Testing Time:  0.0015766746119449014 (+/-) 0.0005872146228732684
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  10.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=10, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 10
No. of parameters : 60

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 10
No. of output : 2
No. of parameters : 22
Dynamic laerning rate for each hidden layer:  [0.02]
N/A% (0 of 20) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.09473684210526 (+/-) 20.698753425068876
Testing Loss:  0.5431874182663465 (+/-) 0.3214055440872702
Precision:  0.5943598448753463
Recall:  0.7709473684210526
F1 score:  0.6712337763095327
Testing Time:  0.0014715194702148438 (+/-) 0.0005958592622866412
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=3, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 3
No. of parameters : 18

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 3
No. of output : 2
No. of parameters : 8
Dynamic laerning rate for each hidden layer:  [0.02]
N/A% (0 of 20) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.09473684210526 (+/-) 20.698753425068876
Testing Loss:  0.5280480431882959 (+/-) 0.26261602505832626
Precision:  0.5943598448753463
Recall:  0.7709473684210526
F1 score:  0.6712337763095327
Testing Time:  0.0016278091229890521 (+/-) 0.0005813392528768669
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 7
No. of parameters : 42

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
N/A% (0 of 20) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.09473684210526 (+/-) 20.698753425068876
Testing Loss:  0.5729194726598891 (+/-) 0.38271157369239533
Precision:  0.5943598448753463
Recall:  0.7709473684210526
F1 score:  0.6712337763095327
Testing Time:  0.001837215925517835 (+/-) 0.0007423172491265569
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  9.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 9
No. of parameters : 54

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 9
No. of output : 2
No. of parameters : 20
Dynamic laerning rate for each hidden layer:  [0.02]
N/A% (0 of 20) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.09473684210526 (+/-) 20.698753425068876
Testing Loss:  0.4863303711539821 (+/-) 0.2862979696687378
Precision:  0.5943598448753463
Recall:  0.7709473684210526
F1 score:  0.6712337763095327
Testing Time:  0.0014715320185611123 (+/-) 0.000678633384282552
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=5, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 5
No. of nodes : 6
No. of parameters : 36

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]

========== Performance occupancy ==========
Preq Accuracy:  77.09 (+/-) 0.0
F1 score:  0.67 (+/-) 0.0
Precision:  0.59 (+/-) 0.0
Recall:  0.77 (+/-) 0.0
Training time:  0.0 (+/-) 0.0
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.0 (+/-) 0.0
Number of features:  7.0 (+/-) 2.45
In [2]:
%run NADINE_classification_creditcarddefault.ipynb
Number of input:  24
Number of output:  2
Number of batch:  30
All Data
100% (30 of 30) |########################| Elapsed Time: 0:00:53 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.70344827586207 (+/-) 2.6260607391957453
Testing Loss:  0.4876625393999034 (+/-) 0.034046624501000204
Precision:  0.779108400805486
Recall:  0.7970344827586207
F1 score:  0.7409359345760664
Testing Time:  0.0022601423592403017 (+/-) 0.0005126260840128597
Training Time:  1.8320883635816902 (+/-) 0.17969217116094482


=== Average network evolution ===
Total hidden node:  8.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 8
No. of parameters : 200

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:48 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.40689655172413 (+/-) 2.3881793721235414
Testing Loss:  0.48319352495259255 (+/-) 0.03468998446802365
Precision:  0.7785146352377841
Recall:  0.7940689655172414
F1 score:  0.7311199269812312
Testing Time:  0.0021869725194470636 (+/-) 0.0006640042668574176
Training Time:  1.6570927439064815 (+/-) 0.07569471133639247


=== Average network evolution ===
Total hidden node:  7.413793103448276 (+/-) 0.49251230541674823
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 8
No. of parameters : 200

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:49 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.2655172413793 (+/-) 2.774051669218841
Testing Loss:  0.488578534331815 (+/-) 0.03856925654889938
Precision:  0.7756216598111582
Recall:  0.7926551724137931
F1 score:  0.728010691098317
Testing Time:  0.002055003725249192 (+/-) 0.0004413418572183541
Training Time:  1.692105169953971 (+/-) 0.07951530974987778


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 7
No. of parameters : 175

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:48 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.95172413793104 (+/-) 2.4609530939854727
Testing Loss:  0.48045521152430565 (+/-) 0.03314798795540664
Precision:  0.7794362771483293
Recall:  0.7995172413793104
F1 score:  0.7498015398817669
Testing Time:  0.00240176299522663 (+/-) 0.000614933402361267
Training Time:  1.6598528500260978 (+/-) 0.05700683132292424


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 7
No. of parameters : 175

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:53 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.37931034482759 (+/-) 2.6367926324314372
Testing Loss:  0.4852180213763796 (+/-) 0.036050494308274665
Precision:  0.7739958725871223
Recall:  0.7937931034482759
F1 score:  0.7332104440612969
Testing Time:  0.0021968545584843077 (+/-) 0.0005484229798818605
Training Time:  1.8407955991810765 (+/-) 0.12543995829021518


=== Average network evolution ===
Total hidden node:  7.413793103448276 (+/-) 0.49251230541674823
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 8
No. of parameters : 200

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]

========== Performance creditcarddefault ==========
Preq Accuracy:  79.54 (+/-) 0.25
F1 score:  0.74 (+/-) 0.01
Precision:  0.78 (+/-) 0.0
Recall:  0.8 (+/-) 0.0
Training time:  1.74 (+/-) 0.08
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.0 (+/-) 0.0
Number of features:  7.6 (+/-) 0.49
50% Data
100% (30 of 30) |########################| Elapsed Time: 0:00:26 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.71379310344827 (+/-) 2.6797740689547123
Testing Loss:  0.49564206086356066 (+/-) 0.04097241479918927
Precision:  0.7810204599131659
Recall:  0.7871379310344827
F1 score:  0.7079674062215365
Testing Time:  0.002222949060900458 (+/-) 0.0005032427139756378
Training Time:  0.8979875548132534 (+/-) 0.05336606353053322


=== Average network evolution ===
Total hidden node:  8.448275862068966 (+/-) 0.49731741730537776
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 9
No. of parameters : 225

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 9
No. of output : 2
No. of parameters : 20
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:25 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.72068965517242 (+/-) 2.6511388977966095
Testing Loss:  0.4994287048948222 (+/-) 0.037873722835753916
Precision:  0.7722776146913933
Recall:  0.7872068965517242
F1 score:  0.7107548640785002
Testing Time:  0.002148052741741312 (+/-) 0.0003801168121774236
Training Time:  0.8738716552997458 (+/-) 0.04450860471135495


=== Average network evolution ===
Total hidden node:  8.724137931034482 (+/-) 0.4469476343729558
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 9
No. of parameters : 225

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 9
No. of output : 2
No. of parameters : 20
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:26 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.81724137931036 (+/-) 2.9047011794220805
Testing Loss:  0.4969960122272886 (+/-) 0.03326975250758636
Precision:  0.7688784487331931
Recall:  0.7881724137931034
F1 score:  0.7156120515933921
Testing Time:  0.0018695140707081761 (+/-) 0.0006088282308082159
Training Time:  0.9034510563159811 (+/-) 0.05763563938445578


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=4, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 4
No. of parameters : 100

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of output : 2
No. of parameters : 10
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:25 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.83793103448275 (+/-) 2.6816636361245467
Testing Loss:  0.4930659645590289 (+/-) 0.03484018083557293
Precision:  0.7702990128734366
Recall:  0.7883793103448276
F1 score:  0.7156915108240074
Testing Time:  0.0020897799524767645 (+/-) 0.0007124881861494125
Training Time:  0.8853979768424198 (+/-) 0.062073183108780176


=== Average network evolution ===
Total hidden node:  7.586206896551724 (+/-) 0.49251230541674823
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 8
No. of parameters : 200

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:27 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.1655172413793 (+/-) 2.671209962450695
Testing Loss:  0.49398861465782956 (+/-) 0.03174511796041849
Precision:  0.7700824513352108
Recall:  0.7916551724137931
F1 score:  0.7280565683385404
Testing Time:  0.002230586676762022 (+/-) 0.0004277909400775066
Training Time:  0.9384993602489603 (+/-) 0.09489851387399385


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 7
No. of parameters : 175

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]

========== Performance creditcarddefault ==========
Preq Accuracy:  78.85 (+/-) 0.16
F1 score:  0.72 (+/-) 0.01
Precision:  0.77 (+/-) 0.0
Recall:  0.79 (+/-) 0.0
Training time:  0.9 (+/-) 0.02
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.0 (+/-) 0.0
Number of features:  7.4 (+/-) 1.85
25% Data
100% (30 of 30) |########################| Elapsed Time: 0:00:13 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.97241379310346 (+/-) 2.474824487224977
Testing Loss:  0.5105584376844866 (+/-) 0.038857379659534445
Precision:  0.776626186625895
Recall:  0.7797241379310345
F1 score:  0.6853532857222552
Testing Time:  0.002296086015372441 (+/-) 0.000532624951457563
Training Time:  0.47777397879238787 (+/-) 0.02501534995768557


=== Average network evolution ===
Total hidden node:  6.793103448275862 (+/-) 0.40508069394726653
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 7
No. of parameters : 175

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:13 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.21034482758623 (+/-) 2.370849524390114
Testing Loss:  0.504766924627896 (+/-) 0.03904537430845272
Precision:  0.7738992890157953
Recall:  0.7821034482758621
F1 score:  0.6932599962246041
Testing Time:  0.0021222870925377154 (+/-) 0.0005105055696071864
Training Time:  0.45900690966639024 (+/-) 0.03577791302815684


=== Average network evolution ===
Total hidden node:  8.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 8
No. of parameters : 200

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:12 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.28620689655172 (+/-) 2.556091323436145
Testing Loss:  0.5013325985135704 (+/-) 0.032063591881526694
Precision:  0.7731704937019902
Recall:  0.7828620689655172
F1 score:  0.6958665174243298
Testing Time:  0.002227709211152175 (+/-) 0.0004250723076319963
Training Time:  0.4289821345230629 (+/-) 0.017447438929185703


=== Average network evolution ===
Total hidden node:  9.068965517241379 (+/-) 0.2533954906327425
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=9, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 9
No. of parameters : 225

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 9
No. of output : 2
No. of parameters : 20
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:12 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.13103448275862 (+/-) 2.3799599313167032
Testing Loss:  0.5030371567298626 (+/-) 0.03631214703609974
Precision:  0.7766233416615541
Recall:  0.7813103448275862
F1 score:  0.690414788367686
Testing Time:  0.0019914035139412716 (+/-) 0.00038310826711397407
Training Time:  0.4396825001157563 (+/-) 0.0386737910206393


=== Average network evolution ===
Total hidden node:  7.206896551724138 (+/-) 0.40508069394726653
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 7
No. of parameters : 175

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
100% (30 of 30) |########################| Elapsed Time: 0:00:13 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.99310344827585 (+/-) 2.482341919053771
Testing Loss:  0.5059115115938515 (+/-) 0.032325487245102744
Precision:  0.7778109165535297
Recall:  0.7799310344827586
F1 score:  0.6859655269971857
Testing Time:  0.0022628553982438713 (+/-) 0.0005836023150720172
Training Time:  0.4661414623260498 (+/-) 0.0409321808077169


=== Average network evolution ===
Total hidden node:  6.379310344827586 (+/-) 0.48521542343000995
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=6, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 6
No. of parameters : 150

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of output : 2
No. of parameters : 14
Dynamic laerning rate for each hidden layer:  [0.02]

========== Performance creditcarddefault ==========
Preq Accuracy:  78.12 (+/-) 0.12
F1 score:  0.69 (+/-) 0.0
Precision:  0.78 (+/-) 0.0
Recall:  0.78 (+/-) 0.0
Training time:  0.45 (+/-) 0.02
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.0 (+/-) 0.0
Number of features:  7.4 (+/-) 1.02
Infinite Delay
N/A% (0 of 30) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.85517241379311 (+/-) 2.505247762115322
Testing Loss:  0.5292846241901661 (+/-) 0.030651198693608425
Precision:  0.6061427871581452
Recall:  0.7785517241379311
F1 score:  0.6816138984678044
Testing Time:  0.0011349710924872037 (+/-) 0.0005689360435162017
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=5, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 5
No. of parameters : 125

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of output : 2
No. of parameters : 12
Dynamic laerning rate for each hidden layer:  [0.02]
 90% (27 of 30) |#####################   | Elapsed Time: 0:00:00 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.85517241379311 (+/-) 2.505247762115322
Testing Loss:  0.5244946222880791 (+/-) 0.028016860389621246
Precision:  0.6061427871581452
Recall:  0.7785517241379311
F1 score:  0.6816138984678044
Testing Time:  0.0016700481546336207 (+/-) 0.0007442008502926363
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  11.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=11, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 11
No. of parameters : 275

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=11, out_features=2, bias=True)
)
No. of inputs : 11
No. of output : 2
No. of parameters : 24
Dynamic laerning rate for each hidden layer:  [0.02]
N/A% (0 of 30) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.85517241379311 (+/-) 2.505247762115322
Testing Loss:  0.5335121370595077 (+/-) 0.03766979184623805
Precision:  0.6061427871581452
Recall:  0.7785517241379311
F1 score:  0.6816138984678044
Testing Time:  0.001272768809877593 (+/-) 0.0004460102877105528
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=7, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 7
No. of parameters : 175

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of output : 2
No. of parameters : 16
Dynamic laerning rate for each hidden layer:  [0.02]
 93% (28 of 30) |######################  | Elapsed Time: 0:00:00 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.85517241379311 (+/-) 2.505247762115322
Testing Loss:  0.5316262460988144 (+/-) 0.03758203434551045
Precision:  0.6061427871581452
Recall:  0.7785517241379311
F1 score:  0.6816138984678044
Testing Time:  0.0013745735431539602 (+/-) 0.0006622602527701685
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  8.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 8
No. of parameters : 200

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]
 93% (28 of 30) |######################  | Elapsed Time: 0:00:00 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.85517241379311 (+/-) 2.505247762115322
Testing Loss:  0.5178975113506975 (+/-) 0.02604676863596477
Precision:  0.6061427871581452
Recall:  0.7785517241379311
F1 score:  0.6816138984678044
Testing Time:  0.0016168150408514615 (+/-) 0.000611088322322515
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  8.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===

 1 -th layer
hiddenLayerBasicNet(
  (linear): Linear(in_features=24, out_features=8, bias=True)
  (activation): Sigmoid()
)
No. of inputs : 24
No. of nodes : 8
No. of parameters : 200

 2 -th layer
outputLayerBasicNet(
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of output : 2
No. of parameters : 18
Dynamic laerning rate for each hidden layer:  [0.02]

========== Performance creditcarddefault ==========
Preq Accuracy:  77.86 (+/-) 0.0
F1 score:  0.68 (+/-) 0.0
Precision:  0.61 (+/-) 0.0
Recall:  0.78 (+/-) 0.0
Training time:  0.0 (+/-) 0.0
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.0 (+/-) 0.0
Number of features:  7.8 (+/-) 1.94
In [ ]: